Predictors of Medical Serious Adverse Events in Hip Fracture Patients Treated With Arthroplasty
Bibliographic record
Abstract
AIM: Patients with hip fractures are often frail with multiple comorbidities and at risk of medical serious adverse events (SAEs). We investigated the HEALTH trial patient population to ascertain predictors of SAEs. METHODS: We performed a multivariable Cox regression analysis. Occurrence of SAEs was included as the dependent variable with 31 potential prognostic factors being included as independent variables. RESULTS: One thousand four hundred forty-one patients were included in this analysis. Three hundred seventy (25.6%) patients suffered from an SAE. The most common events were cardiac (38.4%, n = 105), respiratory (20.8%, n = 77), and neurological (14.1%, n = 77). The majority of SAEs (50.8%, n = 188) occurred in the first 90 days after hip fracture with 35.4% occurring in the first 30 days (n = 131). Body mass index (BMI) between 18.5 and 24.9 compared with BMI between 25 and 29.9 [hazard ratio (HR) 1.32, P = 0.03] and receiving a total hip arthroplasty compared with a bipolar hemiarthroplasty (HR 1.36, P = 0.03) were associated with a higher risk of a medical SAE within 24 months of femoral neck fracture. Age (P = 0.09), use of femoral cement (P = 0.59), and use of canal pressurization (P = 0.37) were not associated with a medical SAE. CONCLUSION: Total hip arthroplasty is associated with more SAEs in the immediate postoperative period, and care should be taken in selecting patients for this treatment compared with a hemiarthroplasty. A higher BMI may be protective in hip fracture patients while age alone does not predict SAEs and neither does the use of femoral cement and/or pressurization. LEVEL OF EVIDENCE: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".