Use of Inductive, Problem-Based Clinical Reasoning Enhances Diagnostic Accuracy in Final-Year Veterinary Students
Bibliographic record
Abstract
Despite tremendous progression in the medical field, levels of diagnostic error remain unacceptably high. Cognitive failures in clinical reasoning are believed to be the major contributor to diagnostic error. There is evidence in the literature that teaching problem-based, inductive reasoning has the potential to improve clinical reasoning skills. In this study, 47 final-year veterinary medicine students at the Royal Veterinary College (RVC) were presented with a complex small animal medicine case. The participants were divided into two groups, one of which received a prioritized problem list in addition to the history, physical exam, and diagnostic test results provided to both groups. The students' written approaches to the case were then analyzed and assigned a diagnostic accuracy score (DAS) and an inductive reasoning score (IRS). The IRS was based on a series of predetermined characteristics consistent with the inductive reasoning framework taught at the RVC. No significant difference was found between the DAS scores of each group, indicating that the provision of a prioritized problem list did not impact diagnostic accuracy. However, a significant positive correlation between the IRS and DAS was illustrated for both groups of students, suggesting increased use of inductive reasoning is associated with increased diagnostic accuracy. These results contribute to a body of research proposing that inductive, problem-based reasoning teaching delivered in an additive model, can enhance the clinical reasoning skills of students and reduce diagnostic error.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".