Bibliographic record
Abstract
In Reply to Croskerry and Tait: We thank Drs. Croskerry and Tait for their interest in our report, which showed that there was a moderately strong inverse relationship between reading time and diagnostic accuracy. Croskerry and Tait interpret these results as “consistent with [the view that] intuitive decisions are more vulnerable to error than those made in the analytic system.” This conclusion derives from their interpretation that solution time reflects problem difficulty. Unfortunately, they miss a crucial point in the methodology: All subjects saw essentially the same cases. Thus, the inverse relation between time and accuracy reflects differences in participants, not cases. Further, in Table 2 we showed that this inverse relationship between time and accuracy holds for 23 of the 25 cases. We agree that there is no exact correspondence between length of time and reliance on System 1 or System 2 thinking. But if one accepts a strict dual-process theory, with a faster, contextual, nonanalytical System 1 and a slower, conceptual, analytical System 2, then faster times are likely a consequence of increased reliance on System 1 processes, and our conclusion—that “we found no support for the assertion that a longer time to diagnosis (typically associated with more deliberate, or System 2, processing) results in fewer errors”—follows directly. We recognize that reading a case is not the same as practice. But to assert without proof that “the exercises in the authors’ experiment do not reflect the actual complex processes involved in patient assessment” is too strong. We remind the authors that virtually all the evidence in support of dual-process theory, both in medicine and in the research program of Kahneman,1 derives from performance on written cases. Furthermore, many of the studies that have looked at the relationship between errors and System 1 and System 2 processes in real-world tasks and stimuli2–3 would support our conclusion that expert clinicians can use rapid and efficient heuristics to solve problems with fewer errors than when they use slower deliberative processes. We are puzzled by the authors’ statement that “faster responses … occur when the participants have the knowledge base to quickly arrive at the correct conclusion using System 2.” Our understanding is that System 1 thinking, based on knowledge related to prior experience, leads to rapid and (usually) correct solutions. Croskerry and Tait appear to assume three processes: slow and correct System 2, fast and error-prone System 1, and fast and correct System 2. This is clearly not what Kahneman,4 one of the founders of dual-processing theories, believes when he states that the operations of System 1 are fast, automatic, effortless, associative, and difficult to control or modify. The operations of System 2 are slower, serial, effortful, and deliberately controlled. Finally, we do not “promote the notion … that speed increases accuracy.” In our report, we stated, “as educators we should not encourage learners to speed up or avoid any reflection.” But we do want to stress that, even if one accepted a strict dual-process theory, System 1 thinking should not be blamed for all diagnostic errors. Jonathan Sherbino, MD Associate professor of emergency medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada. Geoffrey R. Norman, PhD Professor of clinical epidemiology and biostatistics, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada; [email protected]. Wolfgang Gaissmaier, PhD Chief research scientist, Max Planck Institute for Human Development, Harding Center for Risk Literacy, Berlin, Germany.
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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 | 0.018 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.020 | 0.035 |
| Insufficient payload (model declined to judge) | 0.044 | 0.022 |
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".