Risedronate Prevents New Vertebral Fractures in Postmenopausal Women at High Risk
Bibliographic record
Abstract
Independent risk factors for fracture include advanced age, preexisting fractures, and low bone mineral density. Rised-ronate has been shown in several large trials to be safe and effective for patients with osteoporosis, but its effects in populations at high risk are not well characterized. To determine the effect of risedronate on vertebral fracture in high-risk subjects, we pooled data from two randomized, double-blind studies [Vertebral Efficacy with Risedronate Therapy (VERT) Multinational (VERT-MN) and VERT-North America (VERT-NA)] in 3684 postmenopausal osteoporotic women treated with placebo or risedronate 2.5 or 5 mg/d and analyzed fracture risk in subgroups of subjects at high risk for fracture due to greater age or more prevalent fractures (vs. median for overall study population), or lower bone mineral density (T-score, -2.5 or less). Fractures were diagnosed by quantitative and semiquantitative assessment of radiographs at baseline and 1 yr. In the overall population, treatment for 1 yr with risedronate 5 mg/d reduced the risk of new vertebral fractures by 62% vs. control (relative risk, 0.38; 95% confidence interval, 0.25, 0.56; P < 0.001) and of multiple new vertebral fractures by 90% vs. control (relative risk, 0.10; 95% confidence interval, 0.04, 0.26; P < 0.001). Consistent risk reductions were observed at 1 yr in the risedronate-treated high-risk subgroups. Significant reduction in fracture risk after 1 yr is an important benefit in patients at high risk for fracture because, without treatment, these patients are likely to sustain new fractures in the near term.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, 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".