“Multidisciplinary fast-track” care can significantly reduce risk of mortality among hip fracture patients at least 80 years old: a single-center retrospective study
Bibliographic record
Abstract
BACKGROUND: "Multidisciplinary fast-track" (MFT) care can accelerate recovery and improve prognosis after surgery, but whether it is effective in older people after hip fracture surgery is unclear. METHODS: We retrospectively compared one-year all-cause mortality between hip fracture patients at least 80 years old at our institution who underwent hip fracture surgery between January 2014 and December 2018 and who then received MFT or conventional care. Multivariable regression was used to assess the association between MFT care and mortality after adjustment for confounders. RESULTS: The final analysis included 247 patients who received MFT care and 438 who received conventional orthopedic care. The MFT group showed significantly lower one-year mortality (8.9% vs. 14.4%, P = 0.037). Log-rank testing of Kaplan-Meier survival curves confirmed the survival advantage. However, the two groups did not differ significantly in rates of mortality during hospitalization or at 30 or 90 days after surgery. Regression analysis confirmed that MFT care was associated with lower risk of one-year mortality (hazard ratio [HR] 0.47, 95% confidence interval [CI] 0.281-0.788, P = 0.04), and the survival benefit was confirmed in subgroups of patients with anemia (HR 0.453, 95% CI 0.268-0.767, P = 0.003) and patients with American Society of Anesthesiologists grade III (HR 0.202, 95% CI 0.08-0.51, P = 0.001). CONCLUSIONS: MFT care can reduce one-year mortality among hip fracture patients at least 80 years old. This finding should be verified and extended in multi-center randomized controlled trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".