Population‐based study of the effectiveness of bone‐specific drugs in reducing the risk of osteoporotic fracture
Bibliographic record
Abstract
AIM: Evidence supports bone-specific drugs (BSDs) efficacy in the fracture risk reduction. But treatment rates for osteoporosis among high-risk patients are far below the recommended guidelines. A major concern about BSDs is the lack of adherence with treatment. OBJECTIVE: To determine if BSDs decrease fracture risk in high-risk elderly women in real clinical setting. METHODS: A nested case-control design was used in a cohort of elderly women from the Quebec health databases. Women enter into the cohort if they are 70 years or older between 1995 and 2003. Nested case-controls were designed for women with a diagnosis of osteoporosis (OP) and for those with a prior fracture. All cases of fractures occurring during follow-up were matched with 10 randomly selected controls based on age, time period, bone mass density testing, and having a diagnosis of OP or a prior fracture. Use of BSDs before the index date was categorized as follows: short-term (< or =1 year), intermediate-term (>1 and < or = 3 years), and long-term (>3 years). We used an adjusted conditional logistic regression model to assess BSD effect on fracture. RESULTS: Among 3170 women who had a fracture, of these women, 1824 had OP and 1346 had a prior fracture. Only long-term exposure to BSDs among women with OP reduced the fracture risk by 16% (odds ratio: 0.84; 0.73-0.97). Among women with OP, a high number of medical services or use of anticonvulsants or narcotics increased the fracture risk by 12-73%. Among women with a prior fracture, a high number of medical services or risk of fall or use of benzodiazepines, antidepressants, or narcotics increased the fracture risk by 23-77%. CONCLUSION: The incidence of fractures decreased by 16% among women with OP when more than 80% of BSDs was used for at least 3 years. Among women with a prior fracture, fracture risk reduction was not significant. Exposure to BSDs among women with a prior fracture is troubling, given that only approximately 12% of these individuals were being treated, and only 2% was using BSDs for the long term.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".