Integrating MDT Tumor Board Shadowing into the Undergraduate Medical Curriculum: Perspective of Medical Students.

Site-specific multidisciplinary team (MDT) tumor boards are valuable resources for medical students, enabling them to familiarize themselves with the latest evidence-based cancer management strategies and observe effective teamwork in action. In this study, we looked at the awareness and perceptions of medical students about incorporating MDT tumor boards in the medical curriculum. A cross-sectional study was conducted among medical students from year 1 to year 5 at the Aga Khan University after exemption from ethical review committee. A 20-item self-administered questionnaire was used to evaluate the awareness and perceptions of medical students regarding MDT tumor boards. A total of 285 medical students participated in this study, with their mean age (± standard deviation) being 21.91 ± 1.67 years. A majority of 183 (64.2%) had no prior knowledge of the existence of a site-specific MDT tumor board for cancer management. Of the 285 students, 252 (88.4%) demonstrated sufficient awareness of the effectiveness of MDT tumor boards; similarly, 232 (81.4%) responded positively to the idea of mandatory tumor board rotations being incorporated into the undergraduate curriculum. No significant association was found between the student's year of study (χ2 = 6.03, p = 0.20) or gender (χ2 = 35, p = 0.84) and their perceptions of the effectiveness of MDT tumor boards. However, it was found that students who had prior knowledge of their existence had a stronger association with sufficient awareness (χ2 = 4.2, p = 0.04). The results indicate that while the majority of the medical students have no prior attendance or knowledge regarding MDT tumor boards, there is an overwhelming willingness among students to incorporate them into the medical curriculum.

Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns.

OBJECTIVE: Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due to intensive professional training requirements. In this work, we investigated whether behavioral and physiological signals, extracted from tele-video interviews, differ in individuals with psychiatric disorders. METHODS: Temporal variations in facial expression, vocal expression, linguistic expression, and cardiovascular modulation were extracted from simultaneously recorded audio and video of remote interviews. Averages, standard deviations, and Markovian process-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. RESULTS: Statistically significant feature differences were found between psychiatric and control subjects. Correlations were found between features and self-rated depression and anxiety scores. Heart rate dynamics provided the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities provided AUROCs of 0.72-0.82. CONCLUSION: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. SIGNIFICANCE: The proposed multimodal approach has the potential to facilitate scalable, remote, and low-cost assessment for low-burden automated mental health services.

Acute Oral Mucositis During Hypo-Fractionated Radiation in Squamous Cell Carcinoma of Oral Cavity.

Oral mucositis remains a concern in the treatment of head and neck malignancies. This small study included 11 patients treated by hypo-fractionated radiotherapy and assessed for oral mucositis. All patients received a radiation dose of 55 Gy in 20 fractions (2.75 Gy/fraction). At the end of the first week of radiation, three patients had Grade I oral mucositis. During the last week of radiation, most of the patients developed Grade II and III mucositis, 7 (64%) and 4 (36%), respectively. At one month follow-up, 5 (46%) of them had Grade I, while 2 (18%) had developed Grade II mucositis. At three months, 2 (18%) had Grade I mucositis, and none of the patients showed Grade II/III oral mucositis. Grade II oral mucositis was the most common grade found mainly in the last week of radiation therapy. None had Grade IV oral mucositis. Key Words: Acute oral mucositis, Hypo-fractioned radiation, Oral carcinoma.

Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study.

BACKGROUND: Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE: This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS: Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS: There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS: Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.

A Step towards Personalised Cancer Treatment in Lower Middle-income Countries (LMICS): Molecular Tumour Boards.

Tumour boards are meetings where physicians from various disciplines treating cancer patients meet to recommend evidence-based or the best possible treatment plan. These meetings have evolved with time and now, in every part of the world; site-specific multi-disciplinary tumour boards are established. These meetings are considered pivotal for improving patient outcomes. The advances in molecular and genetic knowledge and technique and their integration in treatment options have paved the way for multiple therapeutic options. However, the adoption of personalised treatment choices is associated with a huge financial burden, especially in low and middle-income countries (LMICs). A molecular tumour board can help to identify and suggest the most appropriate plan of management. Key Words: Molecular, Genetics, Personalised, Challenges.

Multimodal mental health assessment with remote interviews using facial, vocal, linguistic, and cardiovascular patterns.

Objective: The current clinical practice of psychiatric evaluation suffers from subjectivity and bias, and requires highly skilled professionals that are often unavailable or unaffordable. Objective digital biomarkers have shown the potential to address these issues. In this work, we investigated whether behavioral and physiological signals, extracted from remote interviews, provided complimentary information for assessing psychiatric disorders. Methods: Time series of multimodal features were derived from four conceptual modes: facial expression, vocal expression, linguistic expression, and cardiovascular modulation. The features were extracted from simultaneously recorded audio and video of remote interviews using task-specific and foundation models. Averages, standard deviations, and hidden Markov model-derived statistics of these features were computed from 73 subjects. Four binary classification tasks were defined: detecting 1) any clinically-diagnosed psychiatric disorder, 2) major depressive disorder, 3) self-rated depression, and 4) self-rated anxiety. Each modality was evaluated individually and in combination. Results: Statistically significant feature differences were found between controls and subjects with mental health conditions. Correlations were found between features and self-rated depression and anxiety scores. Visual heart rate dynamics achieved the best unimodal performance with areas under the receiver-operator curve (AUROCs) of 0.68-0.75 (depending on the classification task). Combining multiple modalities achieved AUROCs of 0.72-0.82. Features from task-specific models outperformed features from foundation models. Conclusion: Multimodal features extracted from remote interviews revealed informative characteristics of clinically diagnosed and self-rated mental health status. Significance: The proposed multimodal approach has the potential to facilitate objective, remote, and low-cost assessment for low-burden automated mental health services.

Outcomes Of Reconstruction With Vascularized Vs Non Vascularized Bone Graft After Resection Of Bone Tumours- A Systematic Review And Meta-Analysis.

BACKGROUND: Vascularized (VBG) and non-vascularized (NVBG) bone grafting are two crucial biological reconstructive techniques in the management of bone tumours. The objective of this study is to compare the outcomes of reconstruction with vascularized and non-vascularized bone grafts after resection of bone tumours. METHODS: A systematic evaluation of the literature from 2012-2021 was undertaken using the online databases PubMed/Medline, Google Scholar, and Cochrane Library considering only comparative articles with specific outcomes for the restoration of the defect with vascularized and non-vascularized bone graft following the resection of bone tumours. The quality of the research methodology was evaluated using Oxford Quality Scoring System and Newcastle Ottawa Scale for randomized trials and non-randomized comparison research respectively. The SPSS version 23 was used to examine the data that was collected. Musculoskeletal tumour society score (MSTS), bone union time, and complications were the outcomes of this review. RESULTS: Four clinical publications were considered, totalling 178 participants (92 men and 86 women) with 90 patients with VBG and 88 with NVBG. MSTS score and bone union time were the key outcomes that were measured. The overall MSTS (p>0.05) and rate of complications (p>0.05) results were comparable between the two groups, however, VBG had a better rate of bone union (p<0.001). CONCLUSIONS: As a result of the quicker bone union, our systematic evaluation demonstrated that VBG causes earlier recovery. Complication rates and functional results were the same in both groups. The link between the bone union time and functional score following VBG and NVBG must also be demonstrated.

Rationale of Establishment of Cardio-oncology Multi- disciplinary Team Tumour Boards in Pakistan.

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Emotional Intelligence Training can be Incorporated as an Essential Component of Postgraduate Medical Education: Paving the Way Towards the Development of Multidisciplinary Team Culture.

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Importance of Development of Rare Cancers National Tumour Board.

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Deep learning for religious and continent-based toxic content detection and classification.

With time, numerous online communication platforms have emerged that allow people to express themselves, increasing the dissemination of toxic languages, such as racism, sexual harassment, and other negative behaviors that are not accepted in polite society. As a result, toxic language identification in online communication has emerged as a critical application of natural language processing. Numerous academic and industrial researchers have recently researched toxic language identification using machine learning algorithms. However, Nontoxic comments, including particular identification descriptors, such as Muslim, Jewish, White, and Black, were assigned unrealistically high toxicity ratings in several machine learning models. This research analyzes and compares modern deep learning algorithms for multilabel toxic comments classification. We explore two scenarios: the first is a multilabel classification of Religious toxic comments, and the second is a multilabel classification of race or toxic ethnicity comments with various word embeddings (GloVe, Word2vec, and FastText) without word embeddings using an ordinary embedding layer. Experiments show that the CNN model produced the best results for classifying multilabel toxic comments in both scenarios. We compared the outcomes of these modern deep learning model performances in terms of multilabel evaluation metrics.

Solitary rib metastasis from primary nasopharyngeal carcinoma: A rare clinical presentation.

We report a case of a middle age male who presented to our tertiary care university hospital with the complaints of nasal obstruction and decrease hearing. The CT scan of head and neck exhibited a mass in nasopharynx and enlarged bilateral cervical lymph nodes. Biopsy from nasopharynx confirmed the lesion as poorly differentiated non-keratinizing squamous cell carcinoma and staged as cT2N2M0. He received neoadjuvant chemotherapy. Subsequently, he underwent chemo radiation therapy. He represented with left chest wall pain. Imaging confirmed isolated lesion on left sided 6th rib. Rib lesion was resected followed by radiation therapy to surgical bed and systemic treatment. The patient remained disease free for 4.5 years. Later, his disease relapsed, and he died of systemic disease progression. To the best of the author's knowledge, only few cases have been reported with isolated rib metastasis from nasopharyngeal carcinoma and this is the first case in which metastasectomy was considered.

A Novel Benchmark Dataset for COVID-19 Detection during Third Wave in Pakistan.

Coronavirus (COVID-19) is a highly severe infection caused by the severe acute respiratory coronavirus 2 (SARS-CoV-2). The polymerase chain reaction (PCR) test is essential to confirm the COVID-19 infection, but it has certain limitations, including paucity of reagents, is computationally time-consuming, and requires expert clinicians. Clinicians suggest that the PCR test is not a reliable automated COVID-19 patient detection system. This study proposed a machine learning-based approach to evaluate the PCR role in COVID-19 detection. We collect real data containing 603 COVID-19 samples from the Pakistan Institute of Medical Sciences (PIMS) Hospital in Islamabad, Pakistan, during the third COVID-19 wave. The experiments are separated into two sets. The first set comprises 24 features, including PCR test results, whereas the second comprises 24 features without PCR test. The findings demonstrate that the decision tree achieves the best detection rate for positive and negative COVID-19 patients in both scenarios. The findings reveal that PCR does not contribute to detecting COVID-19 patients. The findings also aid in the early detection of COVID-19, mainly when PCR test results are insufficient for diagnosing COVID-19 and help developing countries with a paucity of PCR tests and specialist facilities.