Robot-assisted versus conventional laparoscopic surgery in the treatment of advanced stage endometriosis: a meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the safety and efficacy of robot-assisted laparoscopy (RAL) versus conventional laparoscopy (CL) in the treatment of advanced stage endometriosis. MATERIALS AND METHODS: Utilizing electronic databases (PubMed, Embase, and Elsevier), a systematic literature review was performed between 2008 and 2015 to compare the RAL surgery with CL surgery (CLS) in the treatment of advanced stage endometriosis. According to meta-analysis criteria, two comparative clinical trials were selected. Outcome measures including length of operation, blood loss, operative complications, and the length of hospitalization, were estimated by the RevMan 5.1 software. RESULTS: In the meta-analysis, there were no significant differences in blood loss, complication, and hospital stay between RAL and CL surgeries in the treatment of advanced stage endometriosis. However, RAL surgery required a higher mean operating time than CL surgery (WMD: 73.85, 95% CI: 56.77-90.94; p < 0 .00001). Comparative studies demonstrated that RAL displayed no outstanding advantages. CONCLUSIONS: As a new minimally invasive method, RAL technology is safe and efficient alternative to CL in the treatment of advanced stage endometriosis. The latent benefits of RAL technology for the treatment of advanced stage endometriosis remain uncertain.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".