Deciding About Fast and Slow Decisions
Bibliographic record
Abstract
Two reports in this issue address the important topic of clinical decision making. Dual process theory has emerged as the dominant model for understanding the complex processes that underlie human decision making. This theory distinguishes between the reflexive, autonomous processes that characterize intuitive decision making and the deliberate reasoning of an analytical approach. In this commentary, the authors address the polarization of viewpoints that has developed around the relative merits of the two systems. Although intuitive processes are typically fast and analytical processes slow, speed alone does not distinguish them. In any event, the majority of decisions in clinical medicine are not dependent on very short response times. What does appear relevant to diagnostic ease and accuracy is the degree to which the symptoms of the disease being diagnosed are characteristic ones. There are also concerns around some methodological issues related to research design in this area of enquiry. Reductionist approaches that attempt to isolate dependent variables may create such artificial experimental conditions that both external and ecological validity are sacrificed. Clinical decision making is a complex process with many independent (and interdependent) variables that need to be separated out in a discrete fashion and then reflected on in real time to preserve the fidelity of clinical practice. With these caveats in mind, the authors believe that research in this area should promote a better understanding of clinical practice and teaching by focusing less on the deficiencies of intuitive and analytical systems and more on their adaptive strengths.
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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 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.033 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".