Risk Factors for Treatment Failure With Antiosteoporosis Medication: The Global Longitudinal Study of Osteoporosis in Women (GLOW)
Bibliographic record
Abstract
Antiosteoporosis medication (AOM) does not abolish fracture risk, and some individuals experience multiple fractures while on treatment. Therefore, criteria for treatment failure have recently been defined. Using data from the Global Longitudinal Study of Osteoporosis in Women (GLOW), we analyzed risk factors for treatment failure, defined as sustaining two or more fractures while on AOM. GLOW is a prospective, observational cohort study of women aged ≥55 years sampled from primary care practices in 10 countries. Self-administered questionnaires collected data on patient characteristics, fracture risk factors, previous fractures, AOM use, and health status. Data were analyzed from women who used the same class of AOM continuously over 3 survey years and had data available on fracture occurrence. Multivariable logistic regression was used to identify independent predictors of treatment failure. Data from 26,918 women were available, of whom 5550 were on AOM. During follow-up, 73 of 5550 women in the AOM group (1.3%) and 123 of 21,368 in the non-AOM group (0.6%) reported occurrence of two or more fractures. The following variables were associated with treatment failure: lower Short Form 36 Health Survey (SF-36) score (physical function and vitality) at baseline, higher Fracture Risk Assessment Tool (FRAX) score, falls in the past 12 months, selected comorbid conditions, prior fracture, current use of glucocorticoids, need of arms to assist to standing, and unexplained weight loss ≥10 lb (≥4.5 kg). Three variables remained predictive of treatment failure after multivariable analysis: worse SF-36 vitality score (odds ratio [OR] per 10-point increase, 0.85; 95% confidence interval [CI], 0.76-0.95; p = 0.004); two or more falls in the past year (OR, 2.40; 95% CI, 1.34-4.29; p = 0.011), and prior fracture (OR, 2.93; 95% CI, 1.81-4.75; p < 0.0001). The C statistic for the model was 0.712. Specific strategies for fracture prevention should therefore be developed for this subgroup of patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".