ASSESSING FUNCTIONAL FRACTURE RISK: AN INDEPENDENT PREDICTOR OF INCIDENT FRACTURES AT SKELETAL SITE?
Bibliographic record
Abstract
The purpose of this study was to determine if ‘unsafe’ functional movements as measured by short forms of the Safe Functional Motion test (SFM-6 and SFM-3) predict incident fragility fracture at any skeletal site. An osteoporosis clinic database was queried for adults with baseline SFM scores and corresponding data for prevalent fracture, history of injurious falls, femoral neck bone mineral density (fnBMD), bone-sparing medication use, and incident fracture at 1yr, and 3yr follow-up (n= 1700) Multiple logistic regressions, adjusted for gender, age, history of injurious fall(s), fnBMD, bone-sparing medication use, and any prevalent fracture at baseline to determine whether baseline SFM-6 and SFM-3 scores were associated with fragility fracture at any skeletal site at follow-up. According to the analyses, the SFM-6 score was a significant independent predictor of fracture at any site at 1y (p=0.014), and 3y follow-up (adjusted odds ratio (95%CI) = 1.26 (1.140, 1.396) for each 10 point decrease; p < 0.0001). Similarly, SFM-3 score was also a significant independent predictor of any fracture at 1y (p0.0.01), and 3y follow-up (adjusted odds ratio (95%CI) = 1.183 (1.098, 1.274) for each 10 point decrease; p < 0.0001). For all analyses, no other variables were significant predictors at 1yr. Age and fnBMD also were significant at the 3y follow-up. ‘Unsafe’ movement strategies, as measured using the SFM-3 or SFM-6, increase fracture risk by 18–25% for each 10 point drop in score independent of altered risk associated with age, and bone mineral density
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".