High preoperative D-dimer increases the risk of venous thromboembolism after gynecological tumor surgeries: a meta-analysis of cohort studies
Bibliographic record
Abstract
The role of preoperative D-dimer in the prediction of postoperative venous thromboembolism (VTE) with gynecological tumor remains unclear. This meta-analysis sought to assess the association between preoperative D-dimer and the risk of VTE after gynecological tumor surgeries and to identify prognostic significance of D-dimer in the prediction of postoperative VTE. This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement. Eight electronic databases were searched for cohort studies from the date of inception to April 2024. The Newcastle-Ottawa Scale scoring tool and the Risk of Bias in Non-Randomized Studies-Intervention tool were used to assess the quality of the literature and the risk of bias in cohort studies, respectively. The relative risk and 95% CIs of the highest vs the lowest category and per milligram per liter of D-dimer were pooled relative to the VTE risk after gynecological tumor surgeries. Fifteen studies that met the criteria were included. Among these studies, D-dimer was considered as a continuous variable in 8 studies. The random-effect model results showed that the VTE risk was increased by 42% (15%-69%) per milligram per liter increase in D-dimer. Furthermore, based on the cutoff thresholds of D-dimer, 7 studies that reported the effect estimates of postoperative VTE in women with gynecological tumor by D-dimer were categorized as binary variables. Compared with the reference levels, the pooled relative risk of VTE after gynecological tumor surgeries for the higher level was 2.58 (95% CI, 1.49-4.47). Elevated preoperative D-dimer was associated with higher VTE risks after gynecological tumor surgeries.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.062 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".