Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of energy-based vessel sealing compared with suturing in women undergoing vaginal hysterectomy with respect to surgical outcomes. DATA SOURCES: We searched the Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE. We also screened references from relevant articles and searched trial registries and other sources of unpublished literature. METHODS OF STUDY SELECTION: Randomized controlled trials comparing the use of energy-based vessel sealing devices with traditional suturing of vascular pedicles for vaginal hysterectomy, in women of any age, were included. TABULATION, INTEGRATION, AND RESULTS: Two authors completed independent data extraction and bias assessment of included articles. We used the Grading of Recommendations Assessment, Development and Evaluation methodology to assess bias across studies at the outcome level. Data were pooled based on the random effects model. Seven studies met inclusion criteria (n=662). Energy-based vessel sealing devices decreased operative time by a mean of 17.2 minutes (seven studies, 662 patients; 95% confidence interval [CI] 7.5-27.0) blood loss by a mean of 47.7 mL (five studies, 437 patients; 95% CI 15.5-79.9), drop in hemoglobin by 0.3 g/dL (two studies, 291 patients; 95% CI 0.1-0.6), and postoperative hospital stay by 0.25 days (five studies, 554 patients; 95% CI 0.13-0.37). There was no increase in the rate of complications for energy-based vessel sealing compared with traditional suturing. CONCLUSION: This review suggests that energy-based vessel sealing devices may decrease operating time, blood loss, and hospital stay. There was no difference in complication rate and no studies investigated mortality or quality of life.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".