Length of hospital stay and mortality of hip fracture surgery in patients with Coronavirus disease 2019 (COVID-19) infection: A systematic review and meta-analysis
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) patients who undergo hip fracture surgery are expected to have worse outcomes because they are vulnerable to developing COVID-19-associated complications. The present review attempted to assess the in-hospital and 30-day mortality rates as well as the length of hospital stay in patients with COVID-19 infection who had hip fracture surgery. METHODS: Two authors independently searched Google Scholar, PubMed, Web of Knowledge, SCOPUS, and Embase, based on the MeSH-matched scientific keywords. The nine-star Newcastle-Ottawa Scale (NOS) scoring system was employed to assess the methodological quality of all eligible studies. RESULTS: Eleven cohort studies that included 336 patients comprised the study. Three studies reported in-hospital mortality. Eight studies reported 30-day postoperative mortality. The pooled in-hospital mortality rate was 29.8% (95% CI: 26.6%-35.6%). The pooled 30-day postoperative mortality rate was 35.0% (95% CI: 29.9%-40.5%). The mean hospital stay was 11.29 days (95% CI: 10.65 days-11.94 days). CONCLUSIONS: The rates of in-hospital and 30-day mortality in COVID-19 patients who undergo hip fracture surgery is high. These data suggest delaying hip fracture surgery until COVID-19 infection of the patients is controlled. LEVEL OF EVIDENCE: Level II.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| 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".