The Impact of Frailty Indices on Predicting Complications and Functional Recovery in Proximal Humerus Fractures: A Comparative Study
Bibliographic record
Abstract
Background and Objectives: This retrospective cohort study aimed to evaluate the predictive validity of four frailty indices—Modified Frailty Index-5 (mFI-5), Edmonton Frail Scale (EFS), Clinical Frailty Scale (CFS), and Trauma-Specific Frailty Index (TSFI)—in forecasting postoperative complications and functional outcomes in elderly patients with proximal humerus fractures (PHFs) treated either surgically or conservatively. Materials and Methods: A total of 244 patients aged ≥60 years with PHFs treated at Erzurum Hospital between January 2018 and January 2023 were included. Patients were categorized into surgical (n = 110) and conservative (n = 134) groups. Surgical procedures included open reduction and internal fixation (n = 88), hemiarthroplasty (n = 10), and reverse shoulder arthroplasty (n = 12). Frailty was retrospectively assessed using mFI-5, EFS, CFS, and TSFI based on 24-month follow-up data. Outcomes included complications, reoperations, rehospitalizations, and functional results measured by the American Shoulder and Elbow Surgeons (ASES) score. Results: The overall complication rate was 13.1%, with nonunion being the most common. Reoperation and rehospitalization rates were 10.6% and 20%, respectively. The mean ASES score was 71.3 ± 15.2, with 60% of patients achieving good or excellent outcomes. Frailty scores, particularly mFI-5 and EFS, were significantly higher in the conservatively treated group compared to the surgical group (p < 0.01). Across both treatment modalities, patients with higher frailty scores had significantly increased complication rates; however, this effect was more pronounced in the surgical group. Multivariate logistic regression revealed that mFI-5 significantly predicted complications, reoperations, and rehospitalizations (p < 0.001). EFS was associated with reoperation risk (p = 0.018), while CFS and TSFI were not significantly correlated with any of the outcomes. Conclusions: Among the evaluated indices, mFI-5 showed the strongest predictive accuracy for adverse outcomes in elderly PHF patients. Notably, the negative impact of frailty was more evident among surgically treated patients. Routine frailty assessment may facilitate better risk stratification and individualized treatment planning in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".