Thromboprophylaxis during the Pregnancy-Puerperal Cycle - Literature Review
Bibliographic record
Abstract
Abstract Objective To identify current strategies and recommendations for venous thromboembolism prophylaxis associated with the pregnancy-puerperal cycle, a condition of high morbidity and mortality among women. Methods The literature search was performed between May and October 2019, using the PubMed database, including papers published in Portuguese, English and Spanish. The terms thromboembolism (Mesh) AND pregnancy (Mesh) OR postpartum (Mesh) were used as descriptors, including randomized controlled trials, meta-analyses, systematic reviews and guidelines published from 2009 to 2019, presenting strategies for prevention of thromboembolism during pregnancy and the postpartum. Results Eight articles met the inclusion criteria. Many studies evaluated were excluded because they did not address prevention strategies. We compiled the recommendations from the American Society of Hematologists, the American College of Obstetricians and Gynecologists, the Royal College of Obstetricians and Gynecologists, the Society of Obstetricians and Gynaecologists of Canada, the American College of Chest Physicians and the Royal Australian and New Zealand College of Obstetricians and Gynaecologists. Conclusion: There are some gaps in the research, and clinical studies with appropriate methodology are needed to support decisions made regarding the risk of thromboembolism in the perigestational period. Thus, the attention of the professionals involved in the care of pregnant and postpartum women is crucial, as it is a condition associated with high morbidity and mortality.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".