The “July Effect”: A Look at July Medical Admissions in Teaching Hospitals
Bibliographic record
Abstract
<h3>Purpose:</h3> We examined the effect of admission for myocardial infarction, heart failure, or pneumonia during the first academic quarter compared with all other quarters in teaching versus nonteaching hospitals on length of stay, cost, and mortality. <h3>Methods:</h3> Using data 2011 Nationwide Inpatient Sample, multivariable modeling with an interaction term was used to test teaching hospital effect by academic quarter. Logistic regression was used for mortality and log-transformed linear models for cost and length of stay. <h3>Results:</h3> Charlson Index scores were similar in teaching and nonteaching hospitals. Patients admitted to teaching hospitals for myocardial infarction in the first quarter had a higher risk-adjusted mortality (1.217; confidence interval, 1.147–1.290) than those admitted to a nonteaching hospital during the same quarter (0.849; confidence interval, 0.815–0.885). Mean cost heart failure admissions averaged $584 more, and the mean length of stay was longer (0.10; <i>P</i> = .0127), during the first academic quarter. These effects were not present for quarters 2 through 4. <h3>Conclusions:</h3> This study suggests small increases in mortality among patients admitted with myocardial infarction in the first academic quarter compared with all other quarters in teaching versus nonteaching hospitals. Increased cost and longer stay were seen for those admitted with heart failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".