Impact of frailty on outcomes in geriatric femoral neck fracture management: An analysis of national surgical quality improvement program dataset
Bibliographic record
Abstract
INTRODUCTION: Frailty is a clinical state of increased vulnerability resulting from aging-associated decline in physiologic reserve. Hip fractures are serious fall injuries that affect our aging population. We retrospectively sought to study the effect of frailty on postoperative outcomes after Total Hip Arthroplasty (THA) and Hemiarthroplasty (HA) for femoral neck fracture in a national data set. METHODS: National Surgical Quality Improvement Project dataset (NSQIP) was queried to identify THA and HA for a primary diagnosis femoral neck fracture using ICD-9 codes. Frailty was assessed using the modified frailty index (mFI) derived from the Canadian Study of Health and Aging. The primary outcome was 30-day mortality and secondary outcomes were 30-day morbidity and failure to rescue (FTR). We used multivariate logistic regression to estimate odds ratio for outcomes while controlling for confounders. RESULTS: Of 3121 patients, mean age of patients was 77.34 ± 9.8 years. The overall 30-day mortality was 6.4% (3.2%-THA and 7.2%-HA). One or more severe complications (Clavien-Dindo class-IV) occurred in 7.1% patients (6.7%-THA vs.7.2%-HA). Adjusted odds ratios (ORs) for mortality in the group with the higher than median frailty score were 2 (95%CI, 1.4-3.7) after HA and 3.9 (95%CI, 1.3-11.1) after THA. Similarly, in separate multivariate analysis for Clavien-Dindo Class-IV complications and failure to rescue 1.6 times (CI95% 1.15-2.25) and 2.1 times (CI95% 1.12-3.93) higher odds were noted in above median frailty group. CONCLUSIONS: mFI is an independent predictor of mortality among patients undergoing HA and THA for femoral neck fracture beyond traditional risk factors such as age, ASA class, and other comorbidities. LEVELS 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".