Effectiveness of antiresorptive agents for the prevention of recurrent hip factures
Bibliographic record
Abstract
Osteoporosis is a common condition characterized by bone fragility and fractures. Hip fracture, leads to disability, morbidity, excess mortality and growing costs to health care systems. Antiresorptive agents are used to treat osteoporosis and fractures; it is unknown if these agents are effective in preventing recurrent fractures in individuals who have sustained a hip fracture. Using health services administrative databases, we ascertained the incidence of hip fractures and associated-mortality rates in the elderly population in Quebec, from 1996 to 2002 and, evaluated the effectiveness of antiresorptive agents for the prevention of recurrent hip fractures. We identified 33,243 hip fractures. Age-adjusted annual rates of hip fractures decreased in women by 11% from 1996 to 2002 while they did not change in men. Overall one-year mortality rates were higher in men than in women (37% versus 24%), and remained stable over time. Patients exposed to antiresorptives had a 26% reduction in the rate of recurrent fractures (95% CI, 0.64--0.86) compared to patients who were not exposed to these agents. Hip fractures remain a prevalent disease with serious complications. Further research is essential to confirm our results and, to clarify the association between increasing use of antiresorptive agents and the trend reversal in the incidence of hip fractures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".