Experienced physician descriptions of intuition in clinical reasoning: a typology
Bibliographic record
Abstract
Background Diagnostic intuition is a rapid, non-analytic, unconscious mode of reasoning. A small body of evidence points to the ubiquity of intuition, and its usefulness in generating diagnostic hypotheses and ascertaining severity of illness. Little is known about how experienced physicians understand this phenomenon, and how they work with it in clinical practice. Methods Descriptions of how experienced physicians perceive their use of diagnostic intuition in clinical practice were elicited through interviews conducted with 30 physicians in emergency, internal and family medicine. Each participant was asked to share stories of diagnostic intuition, including times when intuition was both correct and incorrect. Multiple coders conducted descriptive analysis to analyze the salient aspects of these stories. Results Physicians provided descriptions of what diagnostic intuition is, when it occurs and what type of activity it prompts. From stories of correct intuition, a typology of four different types of intuition was identified: Sick/Not Sick, Something Not Right, Frame-shifting and Abduction. Most physician accounts of diagnostic intuition linked this phenomenon to non-analytic reasoning and emphasized the importance of experience in developing a trustworthy sense of intuition that can be used to effectively engage analytic reasoning to evaluate clinical evidence. Conclusions The participants recounted myriad stories of diagnostic intuition that alerted them to unusual diagnoses, previous diagnostic error or deleterious trajectories. While this qualitative study can offer no conclusions about the representativeness of these stories, it suggests that physicians perceive clinical intuition as beneficial for correcting and advancing diagnoses of both common and rare conditions.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".