Clinical teaching and clinical outcomes: teaching capability and its association with patient outcomes
Bibliographic record
Abstract
BACKGROUND: There is little research on the impact of medical education on patient outcome. We studied whether teaching capability is associated with altered short-term patient outcomes. METHODS: We performed a multicentre retrospective cross-sectional study involving 40 clinician teachers who had attended on the general internal medicine services in hospitals affiliated with the University of Toronto along with the clinical outcomes of consecutive patients treated for community-acquired pneumonia, congestive heart failure, chronic obstructive pulmonary disease and gastrointestinal bleeding (n = 4377) between 1999 and 2001. Doctors were characterised by teaching effectiveness scores (n = 677) as high-rated or low-rated according to house staff ratings. RESULTS: There was no correlation between the teaching effectiveness scores and the mean length of stay for those patients treated for community-acquired pneumonia (high-rated = 10.3 versus low-rated = 8.1 days, P = 0.058), congestive heart failure (high-rated = 10.1 versus low-rated = 9.9 days, P = 0.978), chronic obstructive pulmonary disease (high-rated = 9.4 versus low-rated = 9.9 days, P = 0.419) and gastrointestinal bleeding (high-rated = 6.3 versus low-rated = 6.8 days, P = 0.741). In addition, we observed no significant correlation between teaching effectiveness scores and 7-day, 28-day and 1-year readmission rates for all pre-specified diagnoses. CONCLUSION: There is no large correlation between teaching effectiveness scores and short-term patient outcomes, suggesting that doctor teaching capabilities, as perceived by house staff, does not generally impact clinical care.
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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 | 0.002 | 0.015 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".