When Experts Disagree: The Art of Medical Decision Making
Bibliographic record
Abstract
Webcast sponsored by the Irving K. Barber Learning Centre and hosted by the Vancouver Institute Lecture Series. Dr. Jerome Groopman is the Dina and Raphael Recanati Professor of Medicine at Harvard Medical School, Chief of Experimental Medicine at Beth Israel Deaconess Medical Center, and one of the world's leading researchers in cancer and AIDS. He is a staff writer for The New Yorker and has written for The New York Times, The Wall Street Journal, The Washington Post and The New Republic. He is author of The Measure of Our Days; Second Opinions; Anatomy of Hope; the New York Times best seller, How Doctors Think; and the recently released Your Medical Mind. Dr. Pamela Hartzband is a member of the faculty at the Harvard Medical School and the Division of Endocrinology at the Beth Israel Deaconess Medical Center. She is a noted endocrinologist and educator specializing in disorders of the thyroid, adrenal, and pituitary glands and women’s health. She is regularly featured among America’s Best Doctors. She has authored articles in the New England Journal of Medicine on the impact of electronic records, uniform practice guidelines, monetary incentives, and the Internet on the culture of clinical care.
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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 | 0.100 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.072 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".