Bibliographic record
Abstract
OBJECTIVES: This article uses patient-linked data to focus on hospitalization with post-operative infection following cholecystectomy, hysterectomy or appendectomy. The average number of hospital days and the costs of readmission are also estimated. DATA SOURCE: Data for surgeries in fiscal years 1997/98, 1998/99 and 1999/00 are from the Health Person-Oriented Information Database. ANALYTICAL TECHNIQUES: Bivariate tabulations were used to estimate the percentage of patients hospitalized with post-operative infection after cholecystectomy, hysterectomy or appendectomy between 1997/98 and 1999/00. Logistic regression was used to explore associations between infection and patient characteristics, readmission, and peri-operative mortality, while controlling for surgical characteristics. MAIN RESULTS: Hospitalization with post-operative infection was relatively rare, occurring in 1.4% of cholecystectomy, 2.0% of hysterectomy, and 3.8% of appendectomy patients. The associated costs of readmission for post-operative infection for the three surgeries were estimated at 5.4 to 6.3 million dollars annually. Old age, being male, surgical complexity and approach, and diabetes were associated with hospitalization involving a post-operative infection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".