Clinical reasoning processes: unravelling complexity through graphical representation
Bibliographic record
Abstract
CONTEXT: Clinical reasoning is a core skill in medical practice, but remains notoriously difficult for students to grasp and teachers to nurture. To date, an accepted model that adequately captures the complexity of clinical reasoning processes does not exist. Knowledge-modelling software such as mot Plus (Modelling using Typified Objects [MOT]) may be exploited to generate models capable of unravelling some of this complexity. OBJECTIVES: This study was designed to create a comprehensive generic model of clinical reasoning processes that is intended for use by teachers and learners, and to provide data on the validity of the model. METHODS: Using a participatory action research method and the established modelling software (mot Plus), knowledge was extracted and entered into the model by a cognitician in a series of encounters with a group of experienced clinicians over more than 250 contact hours. The model was then refined through an iterative validation process involving the same group of doctors, after which other groups of clinicians were asked to solve a clinical problem involving simulated patients. RESULTS: A hierarchical model depicting the multifaceted processes of clinical reasoning was produced. Validation rounds suggested generalisability across disciplines and situations. CONCLUSIONS: The MOT model of clinical reasoning processes has potentially important applications for use within undergraduate and graduate medical curricula to inform teaching, learning and assessment. Specifically, it could be used to support curricular development because it can help to identify opportune moments for learning specific elements of clinical reasoning. It could also be used to precisely identify and remediate reasoning errors in students, residents and practising doctors with persistent difficulties in clinical reasoning.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".