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Record W2149886543 · doi:10.1097/mcp.0b013e3283061191

Heparin-induced osteoporosis and pregnancy

2008· review· en· W2149886543 on OpenAlexaff
Geneviève Le Templier, Marc Rodger

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2008
Typereview
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineHeparinOsteoporosisPregnancyMEDLINEPharmacologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.194
GPT teacher head0.414
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations62
Published2008
Admission routes1
Has abstractyes

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