Efficacy of risedronate on clinical vertebral fractures within six months
Bibliographic record
Abstract
OBJECTIVE: Postmenopausal osteoporotic women with pre-existing or new incident vertebral fractures are at high risk for future fracture, so prompt treatment is warranted. Risedronate has been shown to reduce the incidence of radiographically-defined vertebral fractures by approximately two-thirds within 1 year. RESEARCH DESIGN: This study examined the effects of risedronate treatment on the time course of the reduction in the risk of clinical vertebral fractures (i.e., symptomatic fractures), on the risk of moderate-to-severe radiographic vertebral fractures, and on height. RESULTS: In 2442 postmenopausal women with prevalent vertebral fractures from the Vertebral Efficacy with Risedronate Therapy (VERT) studies who received either risedronate 5 mg or placebo, daily risedronate reduced the risk of clinical vertebral fractures within 6 months (RR = 0.08, 95% CI 0.01-0.63), and by 69% at 1 year (RR = 0.31, 95% CI 0.12, 0.78). At 1 year, risedronate also reduced the risk of moderate-to-severe radiographically-defined vertebral fractures by 71% (RR = 0.29 95% CI 0.16, 0.54). Height loss was attenuated with treatment, most notably in patients who experienced new vertebral fractures, with a median difference of 0.73 cm compared with subjects receiving placebo (p = 0.005). CONCLUSION: Risedronate reduces the risk of clinical vertebral fractures in postmenopausal women with osteoporosis within 6 months of commencing treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".