The Pronounced Impact of Hip Fractures on Psychosocial Well-being
Bibliographic record
Abstract
BACKGROUND: There are little data on the psychosocial well-being of hip fracture patients. Previous studies lacked a control group to isolate the impact of hip fractures from general aging. We sought to overcome these limitations and quantify the impact of hip fractures on psychosocial well-being. METHODS: We identified a cohort 65 years and older patients who were driving and mobile from the National Health and Aging Trends Study. Participants with exactly one hip fracture between 2011 and 2017 were isolated, and their outcomes an after hip fracture were compared with control subjects via multivariate logistic regression. RESULTS: Hip fracture patients reported a notable decrease in driving frequency and mobility in the first year after fracture, along with an increase in depressive symptomatology and decreased participation in activities. Measures of mobility and activities among survivors returned to peer group levels within 2 years. A larger social network was associated with improved outcomes. DISCUSSION: Hip fracture survivors experience notable declines in function and well-being in the first year after a hip fracture compared with peers. Although mortality is high, surviving patients experience measurable gains in function and well-being in the 3 years after the fracture. These data aid surgeons in counseling families and patients after hip fracture. LEVEL OF EVIDENCE: Level I-prognostic study: inception cohort study. STUDY DESIGN: Prospective-patients enrolled at a uniform point in course of disease and followed over time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".