The effects of organic nitrates on osteoporosis: a randomized controlled trial [ISRCTN94484747]
Bibliographic record
Abstract
BACKGROUND: Osteoporotic fractures are common and are associated with increased morbidity, mortality and health care costs. The most effective way to moderate increases in health care costs and the sickness and premature death associated with osteoporotic fractures, is to prevent osteoporosis. Several lines of evidence suggest that nitrates, drugs typically prescribed for the treatment of angina, may be effective in preventing postmenopausal osteoporosis. METHODS: We have designed a multicentre randomized controlled trial to determine the effects of nitrates on bone. The trial consists of two studies. The objective of the first study is to determine whether isosorbide mononitrate at 20 mg/day or nitroglycerin ointment at 15 mg/day leads to fewer headaches. The nitrate that is best tolerated will be used in a second study with one main objective: To determine if postmenopausal women with a T-score at the lumbar spine (L1 to L4) between 0 and -2.0 randomized to two years of treatment with intermittent nitrates have a greater increase in spine bone mineral density as compared to women randomized to placebo. We hypothesize that: 1. Women will report fewer headaches when they are randomized to intermittent nitroglycerin ointment at 15 mg/day compared to intermittent oral isosorbide mononitrate at 20 mg/day, and, 2. After two years, women randomized to intermittent nitrates will have a greater percent increase in lumbar spine bone mineral density compared with women randomized to placebo. DISCUSSION: We have completed our pilot study and found that transdermal nitroglycerin was associated with fewer headaches than oral isosorbide mononitrate. We are currently recruiting patients for our second main study.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".