Fall prevention in older adults: towards an integrated population-based perspective
Bibliographic record
Abstract
Two articles in the 19 January 2008 BMJ 12 highlight tension between the clinical and public health approaches to fracture prevention among older adults. Jarvinen et al 1 review drug therapy for osteoporosis and conclude that “bone mineral density is a poor predictor of an individual’s fracture risk” and thus that practitioners should focus on fall prevention rather than treatment for osteoporosis as a strategy for fracture prevention. Meanwhile, Gates and colleagues2 note in their systematic review a troubling lack of evidence of efficacy for one respected falls prevention intervention, a multifactorial risk assessment with targeted management at the individual level.34 How can these apparently opposing views be reconciled? Firstly, let’s place the review of Gates et al into a broader context. Their systematic review does not focus on all fall prevention interventions—rather it centers on interventions that screen clinically for fall risk with subsequent action or referral aimed at reducing risk for individuals. The authors note the emergence of fall prevention clinics throughout the UK, and call attention to the lack of evidence in the literature about the optimal location, skill mix, assessment, and interventions these clinics should offer. To be included in the review, an intervention had to: carry out “an assessment of multiple risk factors for falling to identify …
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".