Achieving Quality in Clinical Decision Making: Cognitive Strategies and Detection of Bias
Bibliographic record
Abstract
Clinical decision making is a cornerstone of high-quality care in emergency medicine. The density of decision making is unusually high in this unique milieu, and a combination of strategies has necessarily evolved to manage the load. In addition to the traditional hypothetico-deductive method, emergency physicians use several other approaches, principal among which are heuristics. These cognitive short-cutting strategies are especially adaptive under the time and resource limitations that prevail in many emergency departments (EDs), but occasionally they fail. When they do, we refer to them as cognitive errors. They are costly but highly preventable. It is important that emergency physicians be aware of the nature and extent of these heuristics and biases, or cognitive dispositions to respond (CDRs). Thirty are catalogued in this article, together with descriptions of their properties as well as the impact they have on clinical decision making in the ED. Strategies are delineated in each case, to minimize their occurrence. Detection and recognition of these cognitive phenomena are a first step in achieving cognitive de-biasing to improve clinical decision making in the ED.
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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 | 0.043 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".