OUTCOMES FOLLOWING HIP FRACTURES TREATED IN TEACHING VERSUS COMMUNITY HOSPITALS
Bibliographic record
Abstract
We compared the mortality of hip fracture patients treated in teaching versus community hospitals in Ontario. Hip fracture patients ≥ 50 yrs were identified from the Canadian Institute for Health Information Hospital Discharge Abstracts Database and linked to the Registered Persons Database for death information. Logistic regression analyses were done to assess the relation between hospital type and both mortality and complications after surgery. Covariates examined include sex, age, Charlson-Deyo index, time to surgery and their interactions. Although patients treated in teaching hospitals have more comorbidities and complications they have lower mortality than those treated in community hospitals. To compare the mortality of hip fracture patients treated in teaching versus urban and rural community hospitals in Ontario. Although patients treated in teaching hospitals have more comorbidities and complications they have lower mortality than those treated in community hospitals. This finding will have far-reaching implications for health policy in this province. Hip fracture (ICD-9 code 820) patients ≥ 50 yrs treated in Ontario between 1993 and 1999 were identified from the Canadian Institute for Health Information Hospital Discharge Abstracts Database. These were linked to the Registered Persons Database for death information. Logistic regression analyses were done to assess the relation between hospital type and both mortality and complications after surgery. Covariates examined include sex, age, Charlson-Deyo index, time to surgery and their interactions. Patients treated in teaching hospitals and rural community hospitals were more likely to have a major complication than those in urban community hospitals, adjusted OR (95% CI) 1.37 (1.29–1.45); 1.28 (1.06–1.55) respectively. Patients in teaching hospitals had more comordities than those in community urban or rural hospitals. Nevertheless, patients treated in teaching hospitals have lower mortality (in hospital, and at three, six and twelve months post-surgery) than those in urban community hospitals, adjusted OR (95% CI) 0.76 (0.60–0.96), 0.90 (0.85–0.96), 0.91 (0.86–0.96), 0.92 (0.88–0.96) respectively. The difference between rural and urban community hospitals was not statistically significant, however there was a trend to higher mortality in rural institutions, adjusted OR (95% CI) 0.79 (0.63–1.00), 1.13 (0.95–1.36), 1.16 (0.98–1.36), 1.13 (0.97–1.32) respectively.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".