Bibliographic record
Abstract
We appreciate Mamede and Schmidt’s thoughtful reading of our article. While they raise interesting hypotheses about methodological distinctions across studies, we do not see such differences as “conceptual and methodological shortcomings.” Instead, we think differences between their methodology and ours represent differing perspectives regarding the extent to which one can assume participants’ reasoning strategies based upon the experimental instructions they are given. The authors emphasize the importance of identifying contradictory features prior to generating diagnostic hypotheses; we are not convinced that such sequential representations of reasoning are realistic given the many decades psychologists required to generate separable measures of analytic and nonanalytic processes.1 In medicine, a series of studies have revealed that clinical features are more likely to be seen if one has the relevant diagnosis in mind,2,3 suggesting that reasoning is rarely solely inductive or deductive, and raising questions about the extent to which a clinician could ever be prevented from generating diagnoses when contemplating the features of a case. One of the definitional properties of nonanalytic reasoning is that it is fast, automatic, and largely beyond conscious control.4 This is not to say that the differences between Mamede and Schmidt’s methodology and ours could not account for the differences in results. Rather, it is meant simply to highlight one problem with labeling experimental conditions in these sorts of studies with theoretical constructs (i.e., “automatic reasoning”) rather than strictly limiting oneself to labels based on what was observably done (i.e., instructions to offer a diagnosis based on one’s first impression). The order of instructions may be important, but that is an empirical question that would need to be tested directly. Any such head-to-head comparison of processing interventions should, however, take into account a variety of methodologies suggesting that more analytic, conscious processing—in the absence of a preceding, experimentally induced bias—does not necessarily align with greater diagnostic accuracy.5,6 More generally, in testing the influence of such differences, it will be important to design interventions that are practically meaningful. We favor experimental control, but if the control is so great as to have little real-world value, then the benefit of any instructional intervention will be questionable. Rigid adherence to a reasoning protocol that requires strict researcher oversight is unlikely to be feasible for application in a naturalistic clinical setting. Jonathan S. Ilgen, MD, MCR Assistant professor, Division of Emergency Medicine, University of Washington, School of Medicine, Seattle, Washington; [email protected] Judith L. Bowen, MD Professor, Department of Medicine, Oregon Health & Science University, School of Medicine, Portland, Oregon. Kevin W. Eva, PhD Professor and director of education research and scholarship, Department of Medicine, and senior scientist, Centre for Health Education Scholarship, University of British Columbia, Vancouver, British Columbia, 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 | 0.011 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.044 | 0.094 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".