Bibliographic record
Abstract
To the Editor: In their report in Academic Medicine, Coderre and colleagues1 claimed to prove that “querying an initial diagnosis … allows students to rectify an incorrect diagnosis.” Their study presented students with a series of eight written cases, in a two-part sequence. In the first part, students were given a brief initial scenario, with (as shown by an example included in the report) a chief complaint and some background. The students then provided an initial diagnosis. The experimental manipulation occurred in the second phase, where students were then provided extensive additional information from history, physical findings, and lab tests. In the example, we counted 2 pieces of information in the initial scenario and 23 additional pieces of information. For half the students, the information was “concordant” with the initial data; for half, it was “discordant.” Students were instructed to “consider these additional data before deciding on their final diagnosis.” Most students (84%) provided with concordant data stayed with their first diagnosis. For discordant data, 76% switched to the correct diagnosis after seeing the additional data. The authors concluded that “querying offers a potentially large benefit to diagnostic performance with little associated risk.” Might we suggest an alternative explanation—the “black ball” hypothesis? Providing students with an overwhelming amount of additional data suggesting a different diagnosis would make many students revise their diagnoses automatically. For example, if the first two balls you pull out of a bag are red, you may well consider that the bag contains mostly red balls. If the next 23 balls are red, you are likely near certain that it contains mostly red balls. But if the next 23 balls are black, you will likely change your mind and favor a “mostly black ball” hypothesis. We suspect that someone in this situation needs no encouragement and very little deep thinking to query the red ball hypothesis. Since Coderre and colleagues' intervention completely confounds the effect of introducing a large amount of new data and instruction to query the initial diagnosis, they have insufficient evidence to conclude as they did that instruction to think is the critical factor. Jonathan Sherbino, MD Associate professor, Department of Medicine, McMaster University Faculty of Health Sciences, Ontario, Canada. Geoff Norman, PhD Professor, Department of Clinical Epidemiology and Biostatistics, McMaster University Faculty of Health Sciences, Ontario, Canada; [email protected].
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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 | 0.009 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".