In-hospital mortality following hip fracture care in southern Ontario.
Bibliographic record
Abstract
BACKGROUND: The incidence of hip fractures is increasing within the aging population. We investigated the overall rate of in-hospital mortality following hip fracture and how this mortality rate compares across academic and community hospitals. METHODS: We reviewed prospectively collected data from 17 hospitals in southern Ontario as part of a project to evaluate a new streamlined clinical care pathway developed for acute care of elderly patients with hip fractures. We collected demographic data, prefracture living status, acute care mortality and time to surgery, and we compared these data between community and academic hospitals. RESULTS: Between March 2007 and February 2008, 2178 consecutive patients were admitted with a hip fracture to 13 community and 4 academic hospitals. The mean age was 79 years and 72% were women. The overall in-hospital mortality rate was 5.0%, with no difference between patients treated in academic versus community hospitals (p = 0.56). We found a greater rate of acute care in-hospital mortality for patients admitted from dependent-living facilities compared with those who were living independently (risk ratio 0.63, 95% confidence interval 0.42-0.96). CONCLUSION: Acute care in-hospital mortality following hip fractures remains high and is consistent across academic and community hospitals. With the rising incidence of hip fractures, we need to improve the models of care for these patients to reduce mortality and to maximize functional outcomes while maintaining efficient use of limited health care resources.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".