Incidence of delayed venous thromboembolic events in patients undergoing abdominal and pelvic surgery for cancer: a systematic review and meta‐analysis
Bibliographic record
Abstract
Background Incidence of venous thromboembolism (VTE) following discharge for abdominal cancer surgery is uncertain. Methods We searched MEDLINE and Embase for studies evaluating the incidence of VTE at 3 months from surgery. Studies indicating use of post‐hospital VTE prophylaxis were excluded. Two independent reviewers performed study selection, data abstraction and risk of bias. Random‐effects model was used to estimate pooled incidence, and weights were estimated using inverse variance method. Statistical heterogeneity was explored via subgroup analysis. Results Of 4215 abstracts retrieved, 11 reported the incidence of VTE at 3 months. There were three randomized trials ( n = 520), one prospective cohort study ( n = 284) and seven retrospective cohort studies ( n = 65 308). VTE incidence among prospective studies was 9.6% (95% confidence interval (CI) 2.9–16.4), while for retrospective studies was 2.2% (95% CI 1.4–3.0). Heterogeneity was high ( I 2 = 92% and 81%, respectively). The incidence of symptomatic VTE was 1.3% (95% CI 0.4–2.3) for prospective studies. VTE was diagnosed by screening venography in most of the prospective studies, whereas retrospective studies did not use a screening method. Subgroup analysis based on the type of organ surgery performed explained the heterogeneity. Conclusions VTE incidence following abdominal cancer surgery varies greatly depending on the study type, with differences largely explained by the method of assessment of VTE. The fact that VTE incidence among retrospective studies was closer to the incidence of symptomatic events (non‐screen detected) in the prospective studies, suggests that the screened events were mostly asymptomatic and their clinical significance is unclear.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".