Impact of Simulation Exercises on Total Laparoscopic Hysterectomy Surgical Outcomes: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
BACKGROUND: As resources into gynecological surgical simulation training increase, research showing an association with improved clinical outcomes is needed. OBJECTIVE: To evaluate the association between surgical simulation training for total laparoscopic hysterectomy (TLH) and rates of intraoperative vascular/visceral injury (primary outcome) and operative time. SEARCH STRATEGY: We searched Medline OVID, Embase, Web of Science, Cochrane, and CINAHL databases from the inception of each database to April 5, 2022. Selection Critera: Randomized controlled trials (RCTs) or cohort studies of any size published in English prior to April 4, 2022. DATA COLLECTION AND ANALYSIS: The summary measures were reported as relative risks (RR) or as mean differences (MD) with 95% confidence intervals using the random effects model of DerSimonian and Laird. A Higgins I2 >0% was used to identify heterogeneity. We assessed risk of bias using the Cochrane Risk of Bias tool 2.0 (for RCTs) and the Newcastle Ottawa Scale (for cohort studies). MAIN RESULTS: The primary outcome of this systematic review and meta-analysis was to evaluate the impact of simulation training on the rates of vessel/visceral injury in patients undergoing TLH. Of 989 studies screened 3 (2 cohort studies, 1 randomized controlled trial) met the eligibility criteria for analysis. There was no difference in vessel/visceral injury (OR 1.73, 95% CI 0.53-5.69, p=0.36) and operative time (MD 13.28, 95% CI -6.26 to 32.82, p=0.18) when comparing before and after simulation training. CONCLUSION: There is limited evidence that simulation improves clinical outcomes for patients undergoing TLH.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.004 | 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".