Functional risk for fracture by safe functional motion testing: a short version of the safe functional motion test
Bibliographic record
Abstract
Background: ‘Unsafe’ movement strategies used to perform everyday activities were quantified using scores for tasks included in the Short Form Safe Functional Motion test series (SSFM). Baseline scores were independently associated with incident fractures after adjusting for factors known to effect fracture risk. The purpose of the present study is to determine whether the SSFM, a series of tests of habitual motion, is associated with incident fragility fracture at any skeletal sites. Methods: An osteoporosis clinic database was queried for adults with baseline SSFM scores and corresponding data for prevalent fractures, femoral neck bone mineral density (fnBMD), osteoporosis medication use, and incident fractures at 1-year and 3-year follow ups [ n = 1700 (118 incident fractures) and n = 1058 (202 incident fractures), respectively]. Multiple logistic regressions, adjusted for sex, age, fnBMD, osteoporosis medication use, and any prevalent fractures at baseline, were used to determine whether baseline SSFM scores were associated with incident fragility fractures. Results: An Sfm-3 score was a significant independent predictor of any fracture at 1 year [adjusted odds ratio (95% CI) = 1.118 (1.025, 1.219) for each 10-point decrease in Sfm-3; p = 0.012], and 3-year follow up [adjusted odds ratio (95% CI) = 1.183 (1.098, 1.274) for each 10-point decrease in Sfm-3; p < 0.0001]. Conclusions: Scores on the SSFM predict fracture risk such that for each 10-point drop in score the odds of fracture are increased by up to 18% independent of risk associated with age, bone mineral density, use of bone-sparing medications, and history of a fracture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".