When Starting a MICS Program, Don’t Assume Excellence: Prove It!
Bibliographic record
Abstract
Commentary:When Starting a MICS Program, Don’t Assume Excellence: Prove It!Rachel Eikelboom MD1,2, Rashmi Nedadur MD3,Roberto Vanin Pinto Ribeiro MD3, Bobby Yanagawa MD PhD31 Department of Surgery, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada2 Division of Cardiac Surgery, McMaster University, Hamilton, Ontario, Canada3 Division of Cardiac Surgery, St Michael’s Hospital, University of Toronto, Toronto, Ontario, CanadaCorresponding author:Bobby Yanagawa MD, PhD, FRCSC Program Director, Division of Cardiac Surgery, University of Toronto Assistant Professor, Division of Cardiac Surgery, St. Michael’s Hospital 30 Bond Street, 8th Floor, Bond Wing Toronto, ON M5B 1W8 Canada Tel: 416 864 5706 Fax: 416 864 5031 Email: yanagawab@smh.caWord count: 430Conflict of interest: The authors have no conflict of interest and have not received any funding.Central Figure:
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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 | 0.007 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.042 | 0.069 |
| Insufficient payload (model declined to judge) | 0.028 | 0.019 |
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".