Prevention of Hip Fractures in Long‐Term Care: Relevance of Community‐Derived Data
Bibliographic record
Abstract
Osteoporosis and falling are two major contributing factors to fractures in older persons; the relevant contribution of these may vary according to age, setting, and frailty. The purpose of this review was to examine the existing evidence on osteoporosis treatments to determine whether participants in clinical trials include or resemble the older and frailer adult population living in long-term care (LTC). The trials (N=50) used to support major Canadian guidelines for osteoporosis treatment were reviewed because these are used to recommend treatment for all older adults, and several more-recent studies were added. Trials conducted specifically with participants living in LTC were also reviewed (N=6). The majority of studies (96.0%) on osteoporosis treatments were conducted with community-dwelling participants, with many excluding participants resembling the LTC population. Mean ages ranged from 52 to 84, although for the majority of studies, the mean age was younger than 70. Similarly, 80.0% of studies conducted in LTC included only residents who were ambulatory, mobile, able to transfer independently, or not permanently bedridden. Mean ages in these studies ranged from 83 to 85. These findings suggest that frail older adults, particularly the oldest and frailest adults in LTC, are neglected in clinical trials of osteoporosis fracture prevention. There is little evidence to support the application of community-based guidelines to the LTC population, and studies directly involving this population are needed. The role of age, frailty, and the mechanics of falls in hip fracture are discussed.
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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 | 0.027 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".