Mortality and the Risk Factors in Elderly Female Patients With Femoral Neck and Trochanteric Fractures
Bibliographic record
Abstract
BACKGROUND: The main purpose of this study is to exhaustively explore risk factors, including age, gender, and several clinical indices, for mortality in elderly patients with femoral neck fracture and to evaluate some of them using survival analyses. METHODS: This was a retrospective study tracking 1 year for vital prognosis. Data were collected at post-operation from medical records of the cases. Survival analysis was conducted to investigate the risk factors for death, including albumin, urinary retention, activity of daily living (ADL), and cognitive disorder. RESULTS: We recruited 318 patients with a history of hip surgery carried out at Toyama Municipal Hospital, in which 39 patients died for 1 year after discharge. The results showed a significant decrease in survival rate in low albumin, positive urinary retention, and low ADL (P < 0.01, by log-rank test). The hazard ratios (95% confidence interval) of albumin, urinary retention, ADL, and cognitive disorder were 0.36 (0.19 - 0.69), 0.4 (0.2 - 0.8), 0.29 (0.15 - 0.58) and 0.65 (0.32 - 1.29), respectively. CONCLUSIONS: This study demonstrated that albumin, urinary retention and ADL were the important risk factors for mortality, and suggested that the postoperative management of albumin, urinary retention and ADL is important, especially in elderly female patients receiving surgery of femoral neck and trochanteric fractures.
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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".