Handgrip strength as a predictor of 1‑year mortality after hip fracture surgery in the Colombian Andes Mountains
Bibliographic record
Abstract
Hip fracture is a public health problem recognized worldwide and a potentially catastrophic threat for older persons, even carrying a demonstrated excess of mortality. Handgrip strength (HGS) has been identified as a predictor of different outcomes (mainly mortality and disability) in several groups with hip fracture. PURPOSE: The aim of this study was to determine the association between low HGS and 1-year mortality in a cohort of older patients over 60 years old with fragility hip fractures who underwent surgery in the Colombian Andes Mountains. METHODS: A total of 126 patients (median age 81 years, women 77%) with a fragility hip fracture during 2019-2020 were admitted to a tertiary care hospital. HGS was measured using dynamometry upon admission, and data about sociodemographic, clinical and functional, laboratory, and surgical intervention variables were collected. They were followed up until discharge. Those who survived were contacted by telephone at one, three, and 12 months. Bivariate, multivariate, and Kaplan-Meier analyses with survival curves were performed. RESULTS: The prevalence of low HGS in the cohort was 71.4%, and these patients were older, had poorer functional and cognitive status, higher comorbidity, higher surgical risk, time from admission to surgery > 72 h, lower hemoglobin and albumin values, and greater intra-hospital mortality at one and three months (all p < 0.01). Mortality at one year in in patients with low HGS was 42.2% and 8.3% in those with normal HGS, with a statistically significant difference (p = 0.000). In the multivariate analysis, low HGS and dependent gait measured by Functional Ambulation Classification (FAC) were the factors affecting postoperative 1-year mortality in older adults with hip fractures. CONCLUSION: In this study of older people with fragility hip fractures, low HGS and dependent gait were independent predictive markers of 1-year mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".