Bibliographic record
Abstract
In Reply: We thank Drs. Sherbino and Norman for their thoughtful comments on our manuscript and for proposing an alternative explanation for our results. We have three comments in reply. First, we disagree with the statement that we “claimed to prove.” It would be audacious for us to claim to prove any scientific hypothesis, and particularly one for which there are such conflicting data in the literature. Rather, we went to some lengths to highlight the equipoise in the area of analytic information processing and its contribution to diagnostic performance. Second, we feel the “black ball” hypothesis oversimplifies information processing when diagnosing. There is no role for analytic processing when pulling balls out of a bag and deciding if they are red or black. That is a very different cognitive challenge from interpreting clinical features and the results of investigations to decide which of the many disease processes that can damage the liver is the most likely in an individual patient. Finally, we agree with Drs. Sherbino and Norman that it is always possible that automatic processing, rather than analytic processing, resulted in the students' final diagnosis, correct or incorrect. It is, after all, not possible to switch off automatic processing, and it was not our objective to compare automatic versus analytic processing. Instead, we tried to facilitate the use of analytic processing by our participants in providing them with the type of data that are typically processed analytically, and then to study whether the addition of analytic to automatic processing improved, or hindered, diagnostic performance. Granted, it is not possible to prove that our participants used analytic processing—but previous studies of first-year medical students suggest that they typically process information analytically under these experimental conditions.1,2 Acknowledging that we have as much control over the thinking of students and physicians as we do over the drinking of horses, a more appropriate conclusion from our study is that when we provided additional data—along with instructions to query an initial diagnostic hypothesis in light of those data—performance on discordant cases improved and performance on concordant cases was preserved. Kevin McLaughlin, PhD Assistant dean of undergraduate medical education, Office of Undergraduate Medical Education, University of Calgary, Calgary, Alberta, Canada; [email protected]. Sylvain Coderre, MD Associate professor, Department of Medicine, University of Calgary, Calgary, Alberta, Canada. Bruce Wright, MD Associate dean of undergraduate medical education, Office of Undergraduate Medical Education, University of Calgary, Calgary, Alberta, Canada.
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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 | 0.024 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.029 | 0.043 |
| Insufficient payload (model declined to judge) | 0.013 | 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".