Bibliographic record
Abstract
PURPOSE OF REVIEW: Osteoporosis is the most common serious side effect of long-term unfractionated heparin use. Until recently, it was unknown whether long-term low-molecular-weight heparin was associated with any change in bone mineral density. With increasing long-term low-molecular-weight heparin use, for a variety of indications, this was an important knowledge gap. RECENT FINDINGS: We recently completed an a-priori planned substudy to assess the effect of low-molecular-weight heparin on bone mineral density in an ongoing multicenter multinational randomized trial designed to compare the effect of low-molecular-weight heparin prophylaxis on pregnancy outcomes in thrombophilic pregnant women. The results revealed that there is no significant difference in mean bone mineral density between a low-molecular-weight heparin prophylaxis group and a no prophylaxis group. The study was not adequately powered to detect differences in absolute fracture risk. SUMMARY: Recent results suggest that the use of long-term prophylactic low-molecular-weight heparin in pregnancy is not associated with a significant decrease in bone mineral density. Whether higher doses might be a risk factor for osteoporosis is still an unanswered question. It is also possible that subgroups are more susceptible. Overall, women should be reassured regarding the risk of osteoporosis associated with the use of prophylactic dose of low-molecular-weight heparin during their pregnancy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".