Analysis of Risk Factors and Prognosis of Venous Thromboembolism During Pregnancy and Puerperium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Venous thromboembolism (VTE) during pregnancy and puerperium is one of the important health risks for perinatal women, including deep vein thrombosis (DVT) and pulmonary embolism (PE), which can endanger the lives of mothers and infants in severe cases. Hypercoagulable state during pregnancy, uterine compression, hemodynamic changes and limited postpartum activities all increase the risk of VTE, especially those who have undergone cesarean section and have a history of VTE. There are differences in the prevention strategies of VTE in national guidelines. The Royal College of Obstetricians and Gynecologists (RCOG) adopts a hierarchical management model, while the American College of Obstetricians and Gynecologists (ACOG) is relatively conservative and only recommends drug prevention for high-risk groups, while Canada adopts an intermediate strategy. Based on the characteristics of the Asian population, the domestic expert consensus proposes that localized high-risk factors such as dynamic monitoring of D-dimer, BMI ≥ 28 kg/m², preeclampsia, and assisted reproductive pregnancy be included in the risk assessment system to improve the accuracy of VTE prevention and control. In terms of preventive measures, non-drug interventions (such as early mobilization and gradient compression stockings) are the basis, and low molecular weight heparin (LMWH) is currently recognized as the first choice of anticoagulant drugs, but its timing, dosage and bleeding risk are still controversial. Studies have shown that LMWH can effectively reduce the incidence of VTE, but it may increase the risk of poor postoperative wound healing and bleeding, suggesting that the clinic needs to balance the benefits and risks of prevention, especially in optimizing medication strategies in the general maternal population.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it