Malnutrition, sarcopenia and cachexia: exploring prevalence, overlap, and perceptions in older adults with cancer.

BACKGROUND: Older adults with cancer are a growing population requiring tailored care to achieve optimum treatment outcomes. Their care is complicated by under-recognised and under-treated wasting disorders: malnutrition, sarcopenia, and cachexia. We aimed to investigate the prevalence, overlap, and patients' views and experiences of malnutrition, sarcopenia, and cachexia, in older adults with cancer. METHODS: Mixed-methods study with cross-sectional study and qualitative interviews. Interviews were thematically analysed through a phenomenological lens, with feedback loop analysis investigating relationships between themes and findings synthesised using modified critical interpretative synthesis. FINDINGS: n = 30 were screened for malnutrition, sarcopenia, and cachexia, n = 8 completed semi-structured interviews. Eighteen (60.0%) were malnourished, 16 (53.3%) sarcopenic, and 17 (56.7%) cachexic. One or more condition was seen in 80%, and all three in 30%. In univariate analysis, Rockwood clinical frailty score (OR 2.94 [95% CI: 1.26-6.89, p = 0.013]) was associated with sarcopenia, reported percentage meal consumption (OR 2.28 [95% CI: 1.24-4.19, p = 0.008]), and visible wasting (OR 8.43 [95% CI: 1.9-37.3] p = 0.005) with malnutrition, and percentage monthly weight loss (OR 8.71 [95% CI: 1.87-40.60] p = 0.006) with cachexia. Screening tools identified established conditions rather than 'risk'. Nutritional and functional problems were often overlooked, overshadowed, and misunderstood by both patients and (in patients' perceptions) by clinicians; misattributed to ageing, cancer, or comorbidities. Patients viewed these conditions as both personal impossibilities, yet accepted inevitabilities. CONCLUSION: Perceptions, identification, and management of these conditions needs to improve, and their importance recognised by clinicians and patients so those truly 'at risk' are identified whilst conditions are more remediable to interventions.

The validity of the selection methods for recruitment to UK core psychiatry training: cohort study.

AIMS AND METHOD: Selection into core psychiatry training in the UK uses a computer-delivered Multi-Specialty Recruitment Assessment (MSRA; a situational judgement and clinical problem-solving test) and, previously, a face-to-face Selection Centre. The Selection Centre assessments were suspended during the COVID-19 pandemic. We aimed to evaluate the validity of this selection process using data on 3510 psychiatry applicants. We modelled the ability of the selection scores to predict subsequent performance in the Clinical Assessment of Skills and Competencies (CASC). Sensitivity to demographic characteristics was also estimated. RESULTS: All selection assessment scores demonstrated positive, statistically significant, independent relationships with CASC performance and were sensitive to demographic factors. IMPLICATIONS: All selection components showed independent predictive validity. Re-instituting the Selection Centre assessments could be considered, although the costs, potential advantages and disadvantages should be weighed carefully.

New evidence on the validity of the selection methods for recruitment to general practice training: a cohort study.

BACKGROUND: Selection into UK-based GP training has used the Multi-Specialty Recruitment Assessment (MSRA) and a face-to-face selection centre (SC). The MSRA comprises of a situational judgement test and clinical problem-solving test. The SC was suspended during the COVID-19 pandemic. Evidence is needed to guide national and international selection policy. AIM: To evaluate the validity of GP training selection. DESIGN & SETTING: A retrospective cohort study using data from UK-based national recruitment to GP training, from 2015-2021. METHOD: Data were available for 32 215 GP training applicants. The ability of scores from the specialty selection process to predict subsequent performance in the Clinical Skills Assessment (CSA) of the Membership of the Royal College of General Practitioners examination was modelled using path analysis. The effect sizes for sex, professional family background, and world region of qualification were estimated. RESULTS: All component scores of the selection process demonstrated statistically significant independent relationships with CSA performance (P<0.001), thus establishing their predictive validity. All were sensitive to demographic factors. The SC scores had the weakest relationship with future CSA performance. However, for candidates with MSRA scores below the lowest quartile, the relative contribution of the SC scores to predicting CSA performance was similar to that observed for MSRA components. CONCLUSION: The MSRA has predictive validity in this context. Re-instituting an SC for those with relatively low MSRA scores should be considered. However, the relative costs and potential advantages and disadvantages should be carefully weighed.

Mental health trajectories in university students across the COVID-19 pandemic: findings from the Student Wellbeing at Northern England Universities prospective cohort study.

Introduction: Psychological wellbeing in university students is receiving increased focus. However, to date, few longitudinal studies in this population have been conducted. As such, in 2019, we established the Student Wellbeing At Northern England Universities (SWANS) cohort at the University of York, United Kingdom aiming to measure student mental health and wellbeing every six months. Furthermore, the study period included the COVID-19 pandemic, giving an opportunity to track student wellbeing over time, including over the pandemic. Methods: Eligible participants were invited to participate via email. Data were collected, using Qualtrics, from September 2019 to April 2021, across five waves (W1 to W5). In total, n = 4,622 students participated in at least one wave of the survey. Data collection included sociodemographic, educational, personality measures, and mental health and wellbeing. Latent profile analyses were performed, exploring trajectories of student wellbeing over the study period for those who had completed at least three of the five waves of the survey (n = 765), as measured by the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS). Results: Five latent profile trajectories of student wellbeing were identified. Of these, the two latent classes with initially higher wellbeing scores had broadly stable wellbeing across time (total n = 505, 66%). Two classes had lower initial scores, which lowered further across time (total n = 227, 30%). Additionally, a fifth class of students was identified who improved substantially over the study period, from a mean WEMWBS of 30.4 at W1, to 49.4 at W5 (n = 33, 4%). Risk factors for having less favourable wellbeing trajectories generally included identifying as LGBT+, self-declaring a disability, or previously being diagnosed with a mental health condition. Conclusion: Our findings suggest a mixed picture of the effect of the COVID-19 pandemic on student wellbeing, with a majority showing broadly consistent levels of wellbeing across time, a smaller but still substantial group showing a worsening of wellbeing, and a small group that showed a very marked improvement in wellbeing. Those from groups traditionally underrepresented in higher education were most at risk of poorer wellbeing. This raises questions as to whether future support for wellbeing should target specific student subpopulations.

Knowledge and Response to the COVID-19 Pandemic in People With Severe Mental Illness in Bangladesh and Pakistan: A Cross-Sectional Survey.

BACKGROUND: People with severe mental illnesses (SMIs) are likely to face disproportionate challenges during a pandemic. They may not receive or be able to respond to public health messages to prevent infection or to limit its spread. Additionally, they may be more severely affected, particularly in low- and middle-income countries. METHODS: We conducted a telephone survey (May-June 2020) in a sample of 1,299 people with SMI who had attended national mental health institutes in Bangladesh and Pakistan before the pandemic. We collected information on top worries, socioeconomic impact of the pandemic, knowledge of COVID-19 (symptoms, prevention), and prevention-related practices (social distancing, hygiene). We explored the predictive value of socio-demographic and health-related variables for relative levels of COVID-19 knowledge and practice using regularized logistic regression models. FINDINGS: Mass media were the major source of information about COVID-19. Finances, employment, and physical health were the most frequently mentioned concerns. Overall, participants reported good knowledge and following advice. In Bangladesh, being female and higher levels of health-related quality of life (HRQoL) predicted poor and better knowledge, respectively, while in Pakistan being female predicted better knowledge. Receiving information from television predicted better knowledge in both countries. In Bangladesh, being female, accessing information from multiple media sources, and better HRQoL predicted better practice. In Pakistan, poorer knowledge of COVID-19 prevention measures predicted poorer practice. CONCLUSION: Our paper adds to the literature on people living with SMIs and their knowledge and practices relevant to COVID-19 prevention. Our results emphasize the importance of access to mass and social media for the dissemination of advice and that the likely gendered uptake of both knowledge and practice requires further attention.

Situational judgement test performance and subsequent misconduct in medical students.

INTRODUCTION: Situational judgement tests (SJTs) have been widely adopted, internationally, into medical selection. It was hoped that such assessments could identify candidates likely to exhibit future professional behaviours. Understanding how performance on such tests may predict the risk of disciplinary action during medical school would provide evidence for the validity of such SJTs within student selection. It would also inform the implementation of such tests within student recruitment. METHODS: This cohort study used data for 6910 medical students from 36 UK medical schools who sat the University Clinical Aptitude Test (UCAT) SJT in 2013. The relationship between SJT scores at application and the risk of subsequent disciplinary action during their studies was modelled. The incremental ability of the SJT scores to predict the risk of disciplinary action, above that already provided by UCAT cognitive test scores and secondary (high) school achievement, was also evaluated in 5535 of the students with information available on this latter metric. RESULTS: Two hundred and ten (3.05%) of the students in the cohort experienced disciplinary action. The risk of disciplinary action reduced with increasing performance on the admissions SJT (odds ratio (OR) 0.80, 95% confidence interval (CI) 0.69 to 0.92, p = 0.002). This effect remained similar after adjusting for cognitive performance and prior academic attainment (OR 0.77, 95% CI 0.65 to 0.92, p = 0.004). The overall estimated effect-size was small (Cohen's d = 0.08) and no evidence of 'threshold' effects were observed for the SJT scores and risk of disciplinary action. CONCLUSIONS: Performance on admissions SJTs can, at least modestly, incrementally predict the risk of subsequent disciplinary action, supporting their use in this context. However, for this SJT and outcome, there did not seem a distinct threshold score above which the risk of disciplinary action disproportionately increased. This should be considered when using the scores within medical selection.

Can achievement at medical admission tests predict future performance in postgraduate clinical assessments? A UK-based national cohort study.

OBJECTIVE: To determine whether scores on two undergraduate admissions tests (BioMedical Admissions Test (BMAT) and University Clinical Aptitude Test (UCAT)) predict performance on the postgraduate Membership of the Royal Colleges of Physicians (MRCP) examination, including the clinical examination Practical Assessment of Clinical Examination Skills (PACES). DESIGN: National cohort study. SETTING: Doctors who graduated medical school between 2006 and 2018. PARTICIPANTS: 3045 doctors who had sat BMAT, UCAT and the MRCP. PRIMARY OUTCOME MEASURES: Passing each section of the MRCP at the first attempt, including the clinical assessment PACES. RESULTS: Several BMAT and UCAT subtest scores displayed incremental predictive validity for performance on the first two (written) parts of the MRCP. Only aptitude and skills on BMAT (OR 1.34, 1.08 to 1.67, p=0.01) and verbal reasoning on UCAT (OR 1.34, 1.04 to 1.71, p=0.02) incrementally predicted passing PACES at the first attempt. CONCLUSIONS: Our results imply that the abilities assessed by aptitude and skills and verbal reasoning may be the most important cognitive attributes, of those routinely assessed at selection, for predicting future clinical performance. Selectors may wish to consider placing particular weight on scales assessing these attributes if they wish to select applicants likely to become more competent clinicians. These results are potentially relevant in an international context too, since many admission tests used globally, such as the Medical College Admission Test, assess similar abilities.

Technology Matters: Machine learning approaches to personalised child and adolescent mental health care.

There has been much interest in the potential for machine learning and artificial intelligence to enhance health care. In this article, we discuss the potential applications of the technology to child and adolescent mental health services (CAMHS). We also outline the four key criteria that are likely to be necessary for automated prediction to be translated into clinical benefit. These relate to the choice of task to be automated, the nature of the available data, the methods applied and the context of the system to be implemented.

Predicting relapse or recurrence of depression: systematic review of prognostic models.

BACKGROUND: Relapse and recurrence of depression are common, contributing to the overall burden of depression globally. Accurate prediction of relapse or recurrence while patients are well would allow the identification of high-risk individuals and may effectively guide the allocation of interventions to prevent relapse and recurrence. AIMS: To review prognostic models developed to predict the risk of relapse, recurrence, sustained remission, or recovery in adults with remitted major depressive disorder. METHOD: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2021. We included development and external validation studies of multivariable prognostic models. We assessed risk of bias of included studies using the Prediction model risk of bias assessment tool (PROBAST). RESULTS: We identified 12 eligible prognostic model studies (11 unique prognostic models): 8 model development-only studies, 3 model development and external validation studies and 1 external validation-only study. Multiple estimates of performance measures were not available and meta-analysis was therefore not necessary. Eleven out of the 12 included studies were assessed as being at high overall risk of bias and none examined clinical utility. CONCLUSIONS: Due to high risk of bias of the included studies, poor predictive performance and limited external validation of the models identified, presently available clinical prediction models for relapse and recurrence of depression are not yet sufficiently developed for deploying in clinical settings. There is a need for improved prognosis research in this clinical area and future studies should conform to best practice methodological and reporting guidelines.

Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach.

BACKGROUND: Patients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved. METHODS: We used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13-15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance. RESULTS: The final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration. CONCLUSION: We found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population.

Predictive validity of A-level grades and teacher-predicted grades in UK medical school applicants: a retrospective analysis of administrative data in a time of COVID-19.

OBJECTIVES: To compare in UK medical students the predictive validity of attained A-level grades and teacher-predicted A levels for undergraduate and postgraduate outcomes. Teacher-predicted A-level grades are a plausible proxy for the teacher-estimated grades that replaced UK examinations in 2020 as a result of the COVID-19 pandemic. The study also models the likely future consequences for UK medical schools of replacing public A-level examination grades with teacher-predicted grades. DESIGN: Longitudinal observational study using UK Medical Education Database data. SETTING: UK medical education and training. PARTICIPANTS: Dataset 1: 81 202 medical school applicants in 2010-2018 with predicted and attained A-level grades. Dataset 2: 22 150 18-year-old medical school applicants in 2010-2014 with predicted and attained A-level grades, of whom 12 600 had medical school assessment outcomes and 1340 had postgraduate outcomes available. OUTCOME MEASURES: Undergraduate and postgraduate medical examination results in relation to attained and teacher-predicted A-level results. RESULTS: Dataset 1: teacher-predicted grades were accurate for 48.8% of A levels, overpredicted in 44.7% of cases and underpredicted in 6.5% of cases. Dataset 2: undergraduate and postgraduate outcomes correlated significantly better with attained than with teacher-predicted A-level grades. Modelling suggests that using teacher-estimated grades instead of attained grades will mean that 2020 entrants are more likely to underattain compared with previous years, 13% more gaining the equivalent of the lowest performance decile and 16% fewer reaching the equivalent of the current top decile, with knock-on effects for postgraduate training. CONCLUSIONS: The replacement of attained A-level examination grades with teacher-estimated grades as a result of the COVID-19 pandemic may result in 2020 medical school entrants having somewhat lower academic performance compared with previous years. Medical schools may need to consider additional teaching for entrants who are struggling or who might need extra support for missed aspects of A-level teaching.

The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study.

BACKGROUND: Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. The majority of patients will relapse within 6 months, and those with a history of relapse are more likely to relapse in the future than those with no such history. GPs see a largely undifferentiated case-mix of patients, and once patients with depression reach remission, there is limited guidance to help GPs stratify patients according to risk of relapse. We aim to develop a prognostic model to predict an individual's risk of relapse within 6-8 months of entering remission. The long-term objective is to inform the clinical management of depression after the acute phase. METHODS: We will develop a prognostic model using secondary analysis of individual participant data drawn from seven RCTs and one longitudinal cohort study in primary or community care settings. We will use logistic regression to predict the outcome of relapse of depression within 6-8 months. We plan to include the following established relapse predictors in the model: residual depressive symptoms, number of previous depressive episodes, co-morbid anxiety and severity of index episode. We will use a "full model" development approach, including all available predictors. Performance statistics (optimism-adjusted C-statistic, calibration-in-the-large, calibration slope) and calibration plots (with smoothed calibration curves) will be calculated. Generalisability of predictive performance will be assessed through internal-external cross-validation. Clinical utility will be explored through net benefit analysis. DISCUSSION: We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. Assuming the model has sufficient predictive performance, we outline the next steps including independent external validation and further assessment of clinical utility and impact. STUDY REGISTRATION: ClinicalTrials.gov ID: NCT04666662.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.

BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence. OBJECTIVES: To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery. SEARCH METHODS: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status . SELECTION CRITERIA: We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model. DATA COLLECTION AND ANALYSIS: Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews. MAIN RESULTS: We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model. AUTHORS' CONCLUSIONS: Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Differential attainment in the MRCPsych according to ethnicity and place of qualification between 2013 and 2018: a UK cohort study.

PURPOSE OF THE STUDY: To explore if differential pass rates exist in the clinical component of the UK postgraduate clinical psychiatry exam, the Clinical Assessment of Skills and Competencies (CASC), according to ethnicity and place of qualification (UK vs EEA vs overseas graduates). STUDY DESIGN: Observational study using data from the UK Medical Education Database for 2140 doctors sitting the CASC for the first time between 2013 and 2018. RESULTS: After controlling for age, sex, time of sitting and performance in the written components of the MRCPsych, differences in CASC pass rates persisted between UK graduates self-identifying as Black and Minority Ethnicity (BME) and non-BME (OR for passing 0.36, 95% CI 0.23 to 0.56, p<0.001). Both EEA (OR 0.25, 0.15 to 0.40, p<0.001) and overseas graduates (OR 0.07, 0.05 to 0.11, p<0.001) were less likely to pass the CASC at first attempt, even after controlling for the influence of educational and background variables. These groups, on average, had lower scores on written exams with substantial content relating to procedural skills (eg, critical appraisal) rather than pure recall of factual knowledge. CONCLUSIONS: Substantial differences exist in clinical examination performance between UK BME and non-BME candidates, as well as between UK and non-UK graduates. These differences are not explained by differing levels of clinical knowledge. In the interests of equality, this situation requires further investigation and remediation. Future research should focus on understanding how potential bias may be acting within different stages of recruitment, training and assessment within psychiatry.

Collaborative care for depression in older adults: How much is enough?

Collaborative care in primary care has been shown to be effective for subthreshold depression in older adults in the 'CASPER' trial. However, to understand the impact of adherence, and to explore the minimum effective dose of collaborative care, we reanalysed the trial data using a complier average causal effect (CACE) analysis. Data were available for 705 participants, 519 with 12-month PHQ-9 scores. 'Compliance' could be observed for participants in the intervention group. Latent complier status in the control group was estimated. Completion of five or more sessions of care was defined as 'compliance'. Sensitivity analyses, using alternative cut-offs of two to eight sessions, assessed the impact of changing the definition of 'compliance'. Compliers in the intervention group had lower PHQ-9 scores at 12-month follow up than assumed compliers in the control group (1.75 lower, 95% confidence interval 0.29 to 3.21, p = 0.02), a greater effect than originally reported. Sensitivity analyses confirmed statistically significant differences between the intervention and control groups in those attending five or more sessions. We conclude that collaborative care is causally effective in reducing subthreshold depressive symptoms in older people who adhere to treatment. Our findings suggest the minimum effective dose is five sessions.

Relationship between markers of malnutrition and clinical outcomes in older adults with cancer: systematic review, narrative synthesis and meta-analysis.

Malnutrition predicts poorer clinical outcomes for people with cancer. Older adults with cancer are a complex, growing population at high risk of weight-losing conditions. A number of malnutrition screening tools exist, however the best screening tool for this group is unknown. The aim was to systematically review the published evidence regarding markers and measures of nutritional status in older adults with cancer (age ≥ 70). A systematic search was performed in Ovid Medline, EMBASE, Web of Science, CINAHL, British Nursing Database and Cochrane CENTRAL; search terms related to malnutrition, cancer, older adults. Titles, abstracts and papers were screened and quality-appraised. Data evaluating ability of markers of nutritional status to predict patient outcomes were subjected to meta-analysis or narrative synthesis. Forty-two studies, describing 15 markers were included. Meta-analysis found decreased food intake was associated with mortality (OR 2.15 [2.03-4.20] p = < 0.00001) in univariate analysis. Prognostic Nutritional Index (PNI) was associated with overall survival (HR 1.89 [1.03-3.48] p = 0.04). PNI markers (albumin, total lymphocyte count) could be seen as markers of inflammation rather than nutrition. There a suggested relationship between very low body mass index (BMI) (<18 kg/m2) and clinical outcomes. No tool was identified as appropriate to screen for malnutrition, as distinct from inflammatory causes of weight-loss. Risk of cancer-cachexia and sarcopenia in older adults with cancer limits the tools analysed. Measures of food intake predicted mortality and should be included in clinical enquiry. A screening tool that distinguishes between malnutrition, cachexia and sarcopenia in older adults with cancer is needed.

Situational judgement test validity for selection: A systematic review and meta-analysis.

CONTEXT: Situational judgement tests (SJTs) are widely used to evaluate 'non-academic' abilities in medical applicants. However, there is a lack of understanding of how their predictive validity may vary across contexts. We conducted a systematic review and meta-analysis to synthesise existing evidence relating to the validity of such tools for predicting outcomes relevant to interpersonal workplace performance. METHODS: Searches were conducted in relevant databases to June 2019. Study quality and risk of bias were assessed using the Quality In Prognosis Studies (QUIPS) tool. Results were pooled using random effects meta-analysis and meta-regressions. RESULTS: Initially, 470 articles were identified, 218 title or abstracts were reviewed, and 44 full text articles were assessed with 30 studies meeting the final inclusion criteria and were judged, overall, to be at moderate risk of bias. Of these, 26 reported correlation coefficients relating to validity, with a pooled estimate of 0.32 (95% confidence interval 0.26 to 0.39, P < .0001). Considerable heterogeneity was observed (I2  = 96.5%) with the largest validity coefficients tending to be observed for postgraduate, rather than undergraduate, selection studies (ß = 0.23, 0.11 to 0.36, P < .001). The correction of validity coefficients for attenuation was also independently associated with larger effects (ß = 0.13, 0.03 to 0.23, P = .01). No significant associations with test medium (video vs text format), cross-sectional study design, or period of assessment (one-off vs longer-term) were observed. Where reported, the scores generally demonstrated incremental predictive validity, over and above tests of knowledge and cognitive ability. CONCLUSIONS: The use of SJTs in medical selection is supported by the evidence. The observed trend relating to training stage requires investigation. Further research should focus on developing robust criterion-relevant outcome measures that, ideally, capture interpersonal aspects of typical workplace performance. This will facilitate additional work identifying the optimal place of SJTs within particular selection contexts and further enhancing their effectiveness.

Evaluating the recruitment process into UK anaesthesia core training: a national data linkage study of doctors' performance at selection and subsequent postgraduate training.

PURPOSE OF THE STUDY: To explore which factors increase the likelihood of being deemed appointable to core anaesthesia training in the UK and whether those factors subsequently predict performance in postgraduate training. STUDY DESIGN: Observational study linking UK medical specialty recruitment data with postgraduate educational performance, as measured by Annual Review of Competence Progression (ARCP) outcomes. Data were available for 2782 trainee doctors recruited to anaesthesia core training from 2012 to 2016 with at least one subsequent ARCP outcome. RESULTS: Both higher interview and shortlisting scores were independent and statistically significant (p≤0.001) predictors of more satisfactory ARCP outcomes, even after controlling for the influence of postgraduate exam failure. It was noted that a number of background variables (eg, age at application) were independently associated with the odds of being deemed appointable at recruitment. Of these, increasing age and experience were also negative predictors of subsequent ARCP rating. These influences became statistically non-significant once ARCP outcomes associated with exam failure were excluded. CONCLUSIONS: The predictors of 'appointability' largely also predict subsequent performance in postgraduate training, as indicated by ARCP ratings. This provides evidence for the validity of the selection process. Our results also suggest that greater weight could be applied to shortlisting scores within the overall process of ranking applicants for posts.