Expert-Type Knowledge Structure in Medical Students is Associated With Increased Odds of Diagnostic Success
Bibliographic record
Abstract
BACKGROUND: The relation between knowledge structure and diagnostic performance is unclear. Similarly, variables affecting knowledge structure are poorly understood. PURPOSE: The 1st objective was to examine the relation between concepts in knowledge structure and diagnostic performance. The 2nd objective was to examine the relation between the use of diagnostic schemes by small-group preceptors and knowledge structure of medical students. METHODS: This was a cross-sectional study of 1st-year medical students in 4 clinical presentations: hyponatremia, hyperkalemia, metabolic acidosis, and metabolic alkalosis. The 1st dependent variable was diagnostic success with the number of expert-type concepts in knowledge structure (determined by concept sorting), diagnostic scheme use by preceptors, and clinical presentation as independent variables. The 2nd dependent variable was the number of expert-type concepts in knowledge structure with diagnostic scheme use by preceptors and clinical presentation as independent variables. Data were analyzed using multiple logistic and linear regression. RESULTS: Thirty 1st-year medical students participated. After adjusting for clinical presentation and scheme use by preceptors, the number of expert-type concepts in knowledge structure was associated with increased odds of diagnostic success (odds ratio 1.18 [1.03, 1.35], p = .016). After adjustment for clinical presentation, scheme use by preceptors was associated with increased number of expert-type concepts in knowledge structure (2.22 vs. 1.86, p = .01, d = 0.23). CONCLUSIONS: The number of expert-type concepts in knowledge structure is associated with increased odds of diagnostic success. Scheme use by small-group preceptors is associated with an increased number of expert-type concepts in knowledge structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".