Clinical Reasoning and Threshold Concepts
Bibliographic record
Abstract
To the Editor: We read with interest McBee and colleagues’1 study of resident physicians’ clinical reasoning. Using an established framework of 24 clinical reasoning tasks to analyze residents’ responses to clinical scenarios, they found that use of these tasks occurred in a varied, rather than sequential manner, and that residents described new tasks, such as reprioritization of differential diagnosis, which were not among the original 24.1 The authors applied ecological psychology to their findings, arguing that affordances (opportunities for actions) and effectivities (abilities for action) informed how individual residents reasoned about a given clinical scenario, thus explaining interindividual variability in the order of verbalized reasoning tasks. This study is an important contribution to the growing literature on clinical reasoning processes, and we advocate for a corresponding examination of key knowledge acquisition during residency. Elucidating reasoning processes without parallel consideration of context-specific knowledge evokes what Regehr2 has called “the problem of generalizable solutions.” “Weak” problem-solving routines emphasize processes and are generalizable to many situations but are of limited value for a particular situation, whereas “strong” routines require specific knowledge of a particular situation that may not transfer to other contexts. Identifying and applying threshold concepts may be one way to bridge the gaps between weak and strong routines, and between processes and knowledge in clinical reasoning. Threshold concepts are “portals of entry” into mastery of a discipline and often crystallize around troublesome knowledge.3 For example, “uncertainty” has been identified as a threshold concept in medicine. Residents in the study by McBee and colleagues frequently verbalized diagnostic uncertainty, and learning to work with this uncertainty is key to successful clinical reasoning. Another example would be interpreting a peripheral blood film in a patient with anemia; context-specific knowledge contributes to affordance (availability of a blood film) and effectivity (being able to interpret the blood film findings). Clinical reasoning processes must be coordinated with acquisition and application of essential context-specific knowledge, some of which may be characterized as “threshold concepts,” in order to optimize diagnostic accuracy. Charmaine Ma, MDFirst-year resident, Department of Family Practice, University of British Columbia, Vancouver, British Columbia, Canada; [email protected] Nazlee Tabarsi, MDFirst-year resident, Department of Family Practice, University of British Columbia, Vancouver, British Columbia, Canada. Luke Chen, MD, MMEdClinical associate professor, Division of Hematology and Centre for Health Education Scholarship, University of British Columbia, Vancouver, British Columbia, Canada.
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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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.017 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 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".