Can Change in FRAX Score Be Used to “Treat to Target”? A Population-Based Cohort Study
Bibliographic record
Abstract
It is unknown how responsive the Fracture Risk Assessment (FRAX) tool is to osteoporosis treatment (OTX) or whether it can serve as a target for "goal‐directed" treatment. We studied 11,049 untreated women aged ≥50 years undergoing baseline and follow‐up DXA examinations in Manitoba, Canada. We identified clinical risk factors, intervening OTX based on medication possession ratios (MPR),and incident fractures. FRAX scores for major osteoporotic and hip fractures were computed for each scan using the most current(updated) FRAX inputs. Over 4 years, median FRAX scores showed an increase of 1.1% for major fractures and 0.3% for hip fractures,including women highly adherent to OTX (0.6% and 0.1% increases). Few (2.2%) highly adherent women had a decrease in major fracture probability exceeding 4%, whereas 9.0% had a decrease in hip fracture probability exceeding 1%. Compared with untreated women, OTX was associated with a higher dose‐dependent likelihood of attenuating the expected increase in major fracture risk:adjusted odds ratios (aOR) 2.3 (95% confidence interval [CI] 1.8–2.9) for MPR <0.50; 7.3 (95% CI 5.6–9.6) for MPR 0.50–0.79; and 12.0(95% CI 9.5–15.2) for MPR ≥0.80. In the 4 years after the second DXA scan, 620 (6%) women had major fractures (152 hip fractures). FRAX scores were strongly predictive of incident major fractures (adjusted hazard ratios [aHR] per SD increase in FRAX 1.8, 95%CI 1.7–1.9) and hip fractures (aHR per SD 4.5, 95% CI 3.7–5.7); however, change in FRAX score was not independently associated with major fracture (p=0.8) or hip fracture (p=0.3). In conclusion, FRAX scores slowly increased over time, and this increase was attenuated but not prevented by treatment. Few women had meaningful reductions in FRAX scores, and change in FRAX score did not independently predict incident fracture, suggesting that FRAX with BMD is not responsive enough to be used as a target for goal‐directed treatment.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".