Effects and safety of intraoperative intermittent pneumatic compression for preventing postoperative venous thromboembolism: a meta-analysis
Bibliographic record
Abstract
Introduction Intermittent pneumatic compression (IPC) has been used for venous thrombosis (VTE) prevention. It’s necessary to evaluate the effects and safety of intraoperative use of IPC devices in the prevention of VTE in surgical patients. Material and methods Two authors independently searched the PubMed, Cochrane Library, MedLine, EMbase, China national knowledge infrastructure (CNKI), Wanfang databases for randomized controlled trials (RCTs) and cohort studies on the use of IPC in surgical patients up to June 10, 2021. The Cochrane Collaborations risk of bias tool and Newcastle-Ottawa Scale (NOS) were used for quality assessment. RevMan 5.3 software were used for statistical analyses. Results A total of 13 studies including seven RCTs and six retrospective cohort studies involving 6673 surgical patients were included, 1883 patients underwent IPC intervention. The synthesized RCT results indicated that IPC was beneficial to the reduce the incidence of DVT (RR0.30, 95%CI0.22~0.40, P<0.001) and VTE (RR0.51, 95%CI0.27~0.95, P=0.03). The synthesized results from retrospective cohort studies indicated that IPC is beneficial to the reduce the incidence of DVT (RR0.63, 95%CI0.42~0.96, P=0.03) and PE (RR0.34, 95%CI0.16~0.72, P=0.005). No significant publication biases were found for all synthesized outcomes (all p>0.05). Conclusions IPC seems to be safe and effective in the prevention and management of intraoperative VTE. Limited by sample size, this conclusion still needs to be further confirmed by large-sample, multi-center, high-quality clinical studies.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.072 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".