A circumpolar study unveils a positive non-linear effect of temperature on arctic arthropod availability that may reduce the risk of warming-induced trophic mismatch for breeding shorebirds.

Seasonally abundant arthropods are a crucial food source for many migratory birds that breed in the Arctic. In cold environments, the growth and emergence of arthropods are particularly tied to temperature. Thus, the phenology of arthropods is anticipated to undergo a rapid change in response to a warming climate, potentially leading to a trophic mismatch between migratory insectivorous birds and their prey. Using data from 19 sites spanning a wide temperature gradient from the Subarctic to the High Arctic, we investigated the effects of temperature on the phenology and biomass of arthropods available to shorebirds during their short breeding season at high latitudes. We hypothesized that prolonged exposure to warmer summer temperatures would generate earlier peaks in arthropod biomass, as well as higher peak and seasonal biomass. Across the temperature gradient encompassed by our study sites (>10°C in average summer temperatures), we found a 3-day shift in average peak date for every increment of 80 cumulative thawing degree-days. Interestingly, we found a linear relationship between temperature and arthropod biomass only below temperature thresholds. Higher temperatures were associated with higher peak and seasonal biomass below 106 and 177 cumulative thawing degree-days, respectively, between June 5 and July 15. Beyond these thresholds, no relationship was observed between temperature and arthropod biomass. Our results suggest that prolonged exposure to elevated temperatures can positively influence prey availability for some arctic birds. This positive effect could, in part, stem from changes in arthropod assemblages and may reduce the risk of trophic mismatch.

Why do avian responses to change in Arctic green-up vary?

Global climate change has altered the timing of seasonal events (i.e., phenology) for a diverse range of biota. Within and among species, however, the degree to which alterations in phenology match climate variability differ substantially. To better understand factors driving these differences, we evaluated variation in timing of nesting of eight Arctic-breeding shorebird species at 18 sites over a 23-year period. We used the Normalized Difference Vegetation Index as a proxy to determine the start of spring (SOS) growing season and quantified relationships between SOS and nest initiation dates as a measure of phenological responsiveness. Among species, we tested four life history traits (migration distance, seasonal timing of breeding, female body mass, expected female reproductive effort) as species-level predictors of responsiveness. For one species (Semipalmated Sandpiper), we also evaluated whether responsiveness varied across sites. Although no species in our study completely tracked annual variation in SOS, phenological responses were strongest for Western Sandpipers, Pectoral Sandpipers, and Red Phalaropes. Migration distance was the strongest additional predictor of responsiveness, with longer-distance migrant species generally tracking variation in SOS more closely than species that migrate shorter distances. Semipalmated Sandpipers are a widely distributed species, but adjustments in timing of nesting relative to variability in SOS did not vary across sites, suggesting that different breeding populations of this species were equally responsive to climate cues despite differing migration strategies. Our results unexpectedly show that long-distance migrants are more sensitive to local environmental conditions, which may help them to adapt to ongoing changes in climate.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions.

PURPOSE: Electronic health record (EHR)-based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation methods necessitate transparency into how quality is approached. We describe the application of data quality dimensions in curating EHR-derived oncology RWD. METHODS: A targeted review was conducted to summarize data quality dimensions in frameworks published by the European Medicines Agency, The National Institute for Healthcare and Excellence, US Food and Drug Administration, Duke-Margolis Center for Health Policy, and Patient-Centered Outcomes Research Institute. We then characterized quality processes applied to curation of Flatiron Health RWD, which originate from EHRs of a nationwide network of academic and community cancer clinics, across the summarized quality dimensions. RESULTS: The primary quality dimensions across frameworks were relevance (including subdimensions of availability, sufficiency, and representativeness) and reliability (including subdimensions of accuracy, completeness, provenance, and timeliness). Flatiron Health RWD quality processes were aligned to each dimension. Relevancy to broad or specific use cases is optimized through data set size and variable breadth and depth. Accuracy is addressed using validation approaches, such as comparison with external or internal reference standards or indirect benchmarking, and verification checks for conformance, consistency, and plausibility, selected on the basis of feasibility and criticality of the variable to the intended use case. Completeness is assessed against expected source documentation; provenance by recording data transformation, management procedures, and auditable metadata; and timeliness by setting refresh frequency to minimize data lags. CONCLUSION: Development of high-quality, scaled, EHR-based RWD requires integration of systematic processes across the data lifecycle. Approaches to quality are optimized through knowledge of data sources, curation processes, and use case needs. By addressing quality dimensions from published frameworks, Flatiron Health RWD enable transparency in determining fitness for real-world evidence generation.

Changing your mind about the data: Updating sampling assumptions in inductive inference.

When people use samples of evidence to make inferences, they consider both the sample contents and how the sample was generated ("sampling assumptions"). The current studies examined whether people can update their sampling assumptions - whether they can revise a belief about sample generation that is discovered to be incorrect, and reinterpret old data in light of the new belief. We used a property induction task where learners saw a sample of instances that shared a novel property and then inferred whether it generalized to other items. Assumptions about how the sample was selected were manipulated between conditions: in the property sampling frame condition, items were selected because they shared a property, while in the category sampling frame condition, items were selected because they belonged to a particular category. Experiment 1 found that these frames affected patterns of property generalization regardless of whether they were presented before or after the sample data was observed: in both cases, generalization was narrower under a property than a category frame. In Experiments 2 and 3, an initial category or property frame was presented before the sample, and was later retracted and replaced with the complementary frame. Learners were able to update their beliefs about sample generation, basing their property generalization on the more recent correct frame. These results show that learners can revise incorrect beliefs about data selection and adjust their inductive inferences accordingly.

Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study.

BACKGROUND: The evaluation and management of first-time seizure-like events in children can be difficult because these episodes are not always directly observed and might be epileptic seizures or other conditions (seizure mimics). We aimed to evaluate whether machine learning models using real-world data could predict seizure recurrence after an initial seizure-like event. METHODS: This retrospective cohort study compared models trained and evaluated on two separate datasets between Jan 1, 2010, and Jan 1, 2020: electronic medical records (EMRs) at Boston Children's Hospital and de-identified, patient-level, administrative claims data from the IBM MarketScan research database. The study population comprised patients with an initial diagnosis of either epilepsy or convulsions before the age of 21 years, based on International Classification of Diseases, Clinical Modification (ICD-CM) codes. We compared machine learning-based predictive modelling using structured data (logistic regression and XGBoost) with emerging techniques in natural language processing by use of large language models. FINDINGS: The primary cohort comprised 14 021 patients at Boston Children's Hospital matching inclusion criteria with an initial seizure-like event and the comparison cohort comprised 15 062 patients within the IBM MarketScan research database. Seizure recurrence based on a composite expert-derived definition occurred in 57% of patients at Boston Children's Hospital and 63% of patients within IBM MarketScan. Large language models with additional domain-specific and location-specific pre-training on patients excluded from the study (F1-score 0·826 [95% CI 0·817-0·835], AUC 0·897 [95% CI 0·875-0·913]) performed best. All large language models, including the base model without additional pre-training (F1-score 0·739 [95% CI 0·738-0·741], AUROC 0·846 [95% CI 0·826-0·861]) outperformed models trained with structured data. With structured data only, XGBoost outperformed logistic regression and XGBoost models trained with the Boston Children's Hospital EMR (logistic regression: F1-score 0·650 [95% CI 0·643-0·657], AUC 0·694 [95% CI 0·685-0·705], XGBoost: F1-score 0·679 [0·676-0·683], AUC 0·725 [0·717-0·734]) performed similarly to models trained on the IBM MarketScan database (logistic regression: F1-score 0·596 [0·590-0·601], AUC 0·670 [0·664-0·675], XGBoost: F1-score 0·678 [0·668-0·687], AUC 0·710 [0·703-0·714]). INTERPRETATION: Physician's clinical notes about an initial seizure-like event include substantial signals for prediction of seizure recurrence, and additional domain-specific and location-specific pre-training can significantly improve the performance of clinical large language models, even for specialised cohorts. FUNDING: UCB, National Institute of Neurological Disorders and Stroke (US National Institutes of Health).

Better flexion and early recovery with medial-stabilized vs single-radius total knee arthroplasty with kinematic alignment: Two-year clinical results.

BACKGROUND: There are few studies comparing outcomes in patients with posterior cruciate ligament-sacrificing single-radius (SR) versus medial-stabilized (MS) knee devices. Both types of implants are designed to maximize deep-flexion and to maintain stability throughout the knee flexion arc. The aim of this study was to determine whether two-year outcomes differ between these two implant groups. METHODS: Two-hundred and ten patients took part in this retrospective cohort single center study. The SR patients (n = 109) were enrolled in one randomized trial, and the MS knees (n = 101) in another. Patient consent and Investigative Review Board approval was obtained. Radiographs and clinical outcomes were gathered preoperatively and at six weeks, six months, one year and two years. RESULTS: There were no statistically significant differences between treatment groups in terms of preoperative demographic characteristics. The MS group had significantly better knee flexion starting at six months postoperative through two years postoperatively (p < 0.05 - p< 0.001). The Knee Society Pain/Motion score was better in the MS group at one year (95.41 vs 90.86, p < 0.002). The Knee Society Pain score was also better in the MS group starting at six weeks through one year (six weeks: 35.3 vs 30, p = 0.007; one year: 46.4 vs 42.4, p = 0.005, respectively). CONCLUSION: The MS group had better clinical outcomes than the SR group, with significantly greater knee flexion from six months through two years, better Knee Society Pain scores at six weeks through one year, and higher Knee Society Pain/Motion scores at six weeks and one year postoperatively. LEVEL OF EVIDENCE: I.

Shiga-Toxin-Producing Strains of Escherichia coli O104:H4 and a Strain of O157:H7, Which Can Cause Human Hemolytic Uremic Syndrome, Differ in Biofilm Formation in the Presence of CO2 and in Their Ability to Grow in a Novel Cell Culture Medium.

One pathogen that commonly causes gastrointestinal illnesses from the consumption of contaminated food is Escherichia coli O157:H7. In 2011 in Germany, however, there was a prominent outbreak of bloody diarrhea with a high incidence of hemolytic uremic syndrome (HUS) caused by an atypical, more virulent E. coli O104:H4 strain. To facilitate the identification of this lesser-known, atypical E. coli O104:H4 strain, we wanted to identify phenotypic differences between it and a strain of O157:H7 in different media and culture conditions. We found that E. coli O104:H4 strains produced considerably more biofilm than the strain of O157:H7 at 37 °C (p = 0.0470-0.0182) Biofilm production was significantly enhanced by the presence of 5% CO2 (p = 0.0348-0.0320). In our study on the innate immune response to the E. coli strains, we used HEK293 cells that express Toll-like receptors (TLRs) 2 or 4. We found that E. coli O104:H4 strains had the ability to grow in a novel HEK293 cell culture medium, while the E. coli O157:H7 strain could not. Thus, we uncovered previously unknown phenotypic properties of E. coli O104:H4 to further differentiate this pathogen from E. coli O157:H7.

Unifying theories of reasoning and decision making.

De Neys offers a welcome departure from the dual-process accounts that have dominated theorizing about reasoning. However, we see little justification for retaining the distinction between intuition and deliberation. Instead, reasoning can be treated as a case of multiple-cue decision making. Reasoning phenomena can then be explained by decision-making models that supply the processing details missing from De Neys's framework.

Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction.

BACKGROUND: Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS: In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS: We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS: Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.

The valves of the heart have flaps that open and close when the heart beats to maintain the flow of blood in the correct direction. Valvular disease, such as backflow or narrowing, puts additional strain upon heart muscles which can lead to heart failure. Usually, these conditions are diagnosed by doing an echocardiogram, an ultrasound scan of the heart and nearby blood vessels. The electrocardiogram (ECG) records the electrical signal generated by the heart and can be obtained more easily. We used deep learning neural networks, self-learning computer algorithms which excel at finding patterns within complex data. This enabled us to develop computer software able to diagnose valvular disease from ECGs. Earlier detection of such disease can help in improving overall outcome, while also reducing costs related to treatment.

Structure, substrate binding and activity of a unique AAA+ protein: the BrxL phage restriction factor.

Bacteriophage exclusion ('BREX') systems are multi-protein complexes encoded by a variety of bacteria and archaea that restrict phage by an unknown mechanism. One BREX factor, termed BrxL, has been noted to display sequence similarity to various AAA+ protein factors including Lon protease. In this study we describe multiple CryoEM structures of BrxL that demonstrate it to be a chambered, ATP-dependent DNA binding protein. The largest BrxL assemblage corresponds to a dimer of heptamers in the absence of bound DNA, versus a dimer of hexamers when DNA is bound in its central pore. The protein displays DNA-dependent ATPase activity, and ATP binding promotes assembly of the complex on DNA. Point mutations within several regions of the protein-DNA complex alter one or more in vitro behaviors and activities, including ATPase activity and ATP-dependent association with DNA. However, only the disruption of the ATPase active site fully eliminates phage restriction, indicating that other mutations can still complement BrxL function within the context of an otherwise intact BREX system. BrxL displays significant structural homology to MCM subunits (the replicative helicase in archaea and eukaryotes), implying that it and other BREX factors may collaborate to disrupt initiation of phage DNA replication.

Breeding populations of Marbled Godwits and Willets have high annual survival and strong site fidelity to managed wetlands.

The Prairie Pothole Region of central Canada supports a diverse community of breeding waterbirds, but many species have declining populations and the demographic mechanisms driving the declines remain unknown. We conducted a 7-year field study during 1995-2001 to investigate the demographic performance of Marbled Godwits (Limosa fedoa) and Willets (Tringa semipalmata) breeding in managed wetlands near Brooks, Alberta. Mark-recapture analyses based on Cormack-Jolly-Seber models revealed that the annual rates of apparent survival for Marbled Godwits ( ϕ ^  = 0.953 ± 0.012SE) and Willets ( ϕ ^  = 0.861 ± 0.015SE) are among the highest rates of survivorship reported for any breeding or nonbreeding population of large-bodied shorebirds. Our estimates of life expectancy for males were comparable to longevity records in godwits (17.3 years ±5.8SE vs. 25-29+ years) and willets (7.7 ± 1.5SE vs. 10+ years). The two species both showed strong breeding site fidelity but differed in rates of mate fidelity. Pairs that reunited and males that switched mates usually nested <300 m from their previous nests, whereas females that switched mates usually moved longer distances >1.1-1.5 km. Returning pairs usually reunited in godwits (85%) but not in willets (28%), possibly because of species differences in adult survival or patterns of migration. Baseline estimates of annual survival for banded-only birds will be useful for evaluating the potential effects of new tracking tags or the environmental changes that have occurred during the past 20 years. Conservation strategies for large-bodied shorebirds should be focused on reduction of exposure to anthropogenic mortality because low rates of natural mortality suggest that losses to collisions at breeding sites or harvest at nonbreeding areas are likely to cause additive mortality.

Affectionate Touch Promotes Shared Positive Activities.

Shared positive activities, such as engaging conversations and interactive play, enhance relationships and buffer the consequences of negative interactions. The current research tested whether affectionate touch (a prime target for intervention) encourages people to prioritize other shared positive activities and to view shared activities more positively. In a pre-registered dyadic diary study of married couples (Study 1), greater affectionate touch on one day predicted increases in shared positive activities concurrently and prospectively. In a pre-registered dyadic experiment (Study 2), a brief affectionate touch intervention increased self-reported (but not observer-rated) shared positive activities immediately and increased shared positive activities over the following week for people who do not typically engage in such activities. Participants assigned to touch (particularly those low in attachment anxiety) also perceived their partners more positively during shared activities. These results suggest that touch may facilitate positive relationship experiences broadly and supports a theoretical model of affectionate touch.

A prospective comparison of total knee arthroplasty using ultra-congruent, condylar-stabilizing, and posterior-stabilized devices implanted with kinematic alignment: better 2-year outcomes with ultra-congruent.

PURPOSE: This study compared the 5-year results of posterior cruciate ligament (PCL)-sacrificing total knee arthroplasty (TKA) with either a post and cam posterior-stabilized (PS) device, a dished, congruent condylar-stabilizing (CS) device, or a deep-dished ultra-congruent (UC) device. The hypothesis was that the clinical and radiographic outcomes would be equivalent. CS and PS participants were part of a prospective, randomized trial, and UC participants were part of a separate prospective, non-randomized protocol that was otherwise identical. A kinematic alignment surgical technique was utilized. METHODS: Participants were assessed preoperatively, and postoperatively at 6 weeks, 6 months, and annually for 5 years by Knee Society Score (KSS), SF-36 v2, Lower Extremity Activity Scale (LEAS), and physical and radiographic evaluation. There were 116 CS/PS participants and 69 UC participants who participated in the study. RESULTS: Tourniquet (P = .02) and operative (P = .01) times for the CS and UC groups were significantly shorter than the PS group. KSS Function scores were better for the UC group than the CS and PS groups at 6 months (P = .04) and 1 year (P = .03), and better in the UC group vs. CS at 2 years (P = .04). The KSS Pain-only score was also better in the UC compared to PS at 6 months (P = .04). There were no significant differences for the KSS Pain/Motion scores, flexion, SF-36, and LEAS scores at any time. CONCLUSION: These data confirm the hypothesis that there are no clinically meaningful significant differences in outcomes between the three groups at a 5-year minimum follow-up, though there is a trend toward less pain and better function at earlier visits in the UC group. LEVEL OF EVIDENCE: II.

Are logical intuitions only make-believe? Reexamining the logic-liking effect.

An ongoing debate in the literature on human reasoning concerns whether or not the logical status (valid vs. invalid) of an argument can be intuitively detected. The finding that conclusions of logically valid inferences are liked more compared to conclusions of logically invalid ones-called the logic-liking effect-is one of the most prominent pieces of evidence in support of this notion. Trippas et al. (2016) found this logic-liking effect for different kinds of inferences, including conditional and categorical syllogisms. However, all invalid conclusions presented by Trippas et al. (2016) were also impossible given the premises and had a particular structure of surface features-that is, an incongruent atmosphere. We present new data from five preregistered experiments in which we replicate the effect reported by Trippas et al. (2016) for conditional and categorical syllogisms but show that this effect is eliminated when controlling for confounds in surface features. Moreover, we present evidence that there is a demand effect at play, which suggests that people are deliberately considering atmosphere cues of an argument to inform their liking ratings. Taken together, the findings of the present study cast doubt on the existence of logical intuitions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Who is sensitive to selection biases in inductive reasoning?

The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both experiments indicated that people were sensitive to the effects of such frames, showing narrower generalization when sample instances were selected because they shared a target property (property sampling) than when instances were sampled because they belonged to a particular group (category sampling). Group generalization patterns conformed to the predictions of a Bayesian model of property induction that incorporates a selective sampling mechanism. In each experiment, however, there was considerable individual variation, with a nontrivial minority showing little sensitivity to sampling frames. Experiment 2 examined correlates of frames sensitivity. A composite measure of working memory capacity predicted individual sensitivity to sampling frames. These results have important implications for current debates about people's ability to factor sample selection mechanisms into their inferences and for the development of formal models of inductive inference. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

What do our sampling assumptions affect: How we encode data or how we reason from it?

In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively distinct inferences on the basis of the same observations. Yet, relatively little is known about how and when these two sources of evidence are combined. Do sampling assumptions affect how the sample contents are encoded, or is any influence exerted only at the point of retrieval when a decision is to be made? We report two experiments aimed at exploring this issue. By systematically varying both the sampling cover story and whether it is given before or after the training stimuli we are able to determine whether encoding or retrieval issues drive the impact of sampling assumptions. We find that the sampling cover story affects generalization when it is presented before the training stimuli, but not after, which suggests that sampling assumptions are integrated during encoding. (PsycInfo Database Record (c) 2023 APA, all rights reserved).