Management Reasoning: Implications for Health Professions Educators and a Research Agenda
Bibliographic record
Abstract
Substantial research has illuminated the clinical reasoning processes involved in diagnosis (diagnostic reasoning). Far less is known about the processes entailed in patient management (management reasoning), including decisions about treatment, further testing, follow-up visits, and allocation of limited resources. The authors' purpose is to articulate key differences between diagnostic and management reasoning, implications for health professions education, and areas of needed research.Diagnostic reasoning focuses primarily on classification (i.e., assigning meaningful labels to a pattern of symptoms, signs, and test results). Management reasoning involves negotiation of a plan and ongoing monitoring/adjustment of that plan. A diagnosis can usually be established as correct or incorrect, whereas there are typically multiple reasonable management approaches. Patient preferences, clinician attitudes, clinical contexts, and logistical constraints should not influence diagnosis, whereas management nearly always involves prioritization among such factors. Diagnostic classifications do not necessarily require direct patient interaction, whereas management prioritizations require communication and negotiation. Diagnoses can be defined at a single time point (given enough information), whereas management decisions are expected to evolve over time. Finally, management is typically more complex than diagnosis.Management reasoning may require educational approaches distinct from those used for diagnostic reasoning, including teaching distinct skills (e.g., negotiating with patients, tolerating uncertainty, and monitoring treatment) and developing assessments that account for underlying reasoning processes and multiple acceptable solutions.Areas of needed research include if and how cognitive processes differ for management and diagnostic reasoning, how and when management reasoning abilities develop, and how to support management reasoning in clinical practice.
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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 | 0.089 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".