Bibliographic record
Abstract
We thank Dr. Eichbaum for the opportunity to elaborate on some of the points we raised in our Perspective. Dr. Eichbaum claims that there is little evidence that errors are caused by knowledge gaps. Actually, several studies demonstrate a relationship between measures of knowledge (indicated by board exam scores) and peer review in practice,1 malpractice claims,2 and coronary care mortality.3 Dr. Eichbaum claims that experience “encompasses the gradual acquisition of complex skills and attributes, such as the metacognitive capacity to monitor and regulate one’s own thinking [and emotions].” Conspicuously absent is evidence to justify this claim. We believe that experience is critically important because it involves a different kind of experiential knowledge, not because it enhances “metacognitive capacity.” Dr. Eichbaum then takes issue with our reliance on written case protocols, failing to mention that much of the evidence used to argue that cognitive biases cause diagnostic errors is also from written cases. Other evidence of cognitive bias comes from retrospective reviews, but these findings are compromised in that reviewers show zero agreement in identifying such biases, and hindsight bias causes them to systematically overestimate the presence of cognitive bias.4 Faced with the absence of evidence that debiasing strategies have any impact on diagnostic errors, Dr. Eichbaum takes refuge in the claim that “debiasing strategies are … difficult to test under general experimental conditions.” Perhaps that is why he finds it necessary to customize instruction to specific contexts. If physicians have to show specific clinical examples of bias to reduce errors in those contexts, that sounds like knowledge to us. No doubt this topic could use more research; for the moment, we remain content with Dr. Eichbaum’s tacit admission that there is no evidence debiasing strategies work. Geoffrey Norman, PhDEmeritus professor, Department of Health Methodology, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; [email protected]Henk G. Schmidt, PhDProfessor, Department of Psychology, Erasmus University, Rotterdam, the Netherlands.Jonathan S. Ilgen, MDAssociate professor, Department of Emergency Medicine, University of Washington, Seattle, Washington.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.030 | 0.075 |
| Insufficient payload (model declined to judge) | 0.009 | 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".