Patient Outcomes in Teaching Versus Nonteaching General Internal Medicine Services
Bibliographic record
Abstract
PURPOSE: Patient care quality appears to be similar when delivered by trainee and attending physicians. The authors conducted a systematic review and meta-analysis to examine whether outcomes differ for general internal medicine (GIM) patients admitted to teaching versus nonteaching services. METHOD: The authors searched Medline, EMBASE, and Cochrane Library databases in May 2012 to identify peer-reviewed, English-language studies with contemporaneous controls comparing inpatient mortality, 30-day readmission rate, and/or length of stay (LOS) for inpatients admitted to teaching or nonteaching GIM services. RESULTS: The 15 included studies (1 randomized controlled trial, 14 observational) included 108,570 patients admitted to U.S. hospitals during 1987-2011. Inpatient mortality did not differ between teaching and nonteaching services (13 studies, 108,015 patients; 2.5% versus 2.8%; OR, 1.07; 95% CI, 0.87-1.32; I = 82%); results were consistent in risk-adjusted studies (adjusted OR, 0.91; 95% CI, 0.76-1.08) and higher-quality studies (OR, 0.94; 95% CI, 0.73-1.21). There were no differences in 30-day readmission rates (11 studies, 106,021 patients; 15.1% versus 13.1%; OR, 1.05; 95% CI, 0.93-1.18). Patients on teaching services appeared to have longer LOS (11 studies, 82,352 patients; unadjusted mean difference, 0.40 days; 95% CI, 0.04-0.77 days), but there was substantial heterogeneity (I = 95%). Differences disappeared in risk-adjusted studies (mean difference: -0.09 days; 95% CI, -0.24 to 0.06 days) and in higher-quality studies (mean difference: -0.05 days; 95% CI, -0.37 to 0.28 days). CONCLUSIONS: There was no convincing evidence that outcomes differed substantively for patients admitted to teaching or nonteaching GIM services.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".