Postoperative mortality in the COVID-positive hip fracture patient, a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: The extent to which concomitant COVID-19 infection increases short-term mortality following hip fracture is not fully understood. A systemic review and meta-analysis of COVID-19 positive hip fracture patients (CPHFPs) undergoing surgery was conducted to explore the association of COVID-19 with short-term mortality. METHODS: Review of the literature identified reports of short-term 30-day postoperative mortality in CPHFPs. For studies including a contemporary control group of COVID-19 negative patients, odds ratios of the association between COVID-19 infection and short-term mortality were calculated. Short-term mortality and the association between COVID-19 infection and short-term mortality were meta-analyzed and stratified by hospital screening type using random effects models. RESULTS: Seventeen reports were identified. The short-term mortality in CPHFPs was 34% (95% C.I., 30-39%). Short-term mortality differed slightly across studies that screened all patients, 30% (95% C.I., 22-39%), compared to studies that conditionally screened patients, 36% (95% C.I., 31-42%), (P = 0.22). The association between COVID-19 infection and short-term mortality produced an odds ratio of 7.16 (95% C.I., 4.99-10.27), and this was lower for studies that screened all patients, 4.08 (95% C.I., 2.31-7.22), compared to studies that conditionally screened patients, 8.32 (95% C.I., 5.68-12.18), (P = 0.04). CONCLUSION: CPHFPs have a short-term mortality rate of 34%. The odds ratio of short-term mortality was significantly higher in studies that screened patients conditionally than in studies that screened all hip fracture patients. This suggests mortality prognostication should consider how COVID-19 infection was identified as asymptomatic patients may fare slightly better.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".