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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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, 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".