Consultants & Trainees Active Participation in Multi-Disciplinary Team (MDT): Tumor Boards can Play a Major Role in the Achievement of their Academic Professional Development Goals
Bibliographic record
Abstract
We wish to share with the readers of this scientific journal our experiences and observations regarding educational gains achieved by the consultant faculty and trainee postgraduate residents via active participation in multidisciplinary team (MDT) Tumor Boards. The rapid modernization of teaching methodologies is being observed in both postgraduate residents and faculty professional education learning processes. Contemporary literature is increasingly depicting academic work which is being directed towards the exploration of both educational and social determinants of health care [1]. Several socio-economic factors have been discussed across the globe which have an impact on the health care systems [2]. Researchers argued over the fact that the leading factor for a positive outcome is socioeconomic wellbeing especially for patients with diseases, like cancer, diabetes mellitus, etc. If we critically evaluate the curriculums designed on the principles of traditional problem-based learning and case-based learning, they are very well-established students centre approaches, but on their merit, they failed to provide a holistic picture of the patient care [3].
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".