Mini-Laparoscopic Versus Conventional Laparoscopic Surgery for Benign Adnexal Masses
Bibliographic record
Abstract
BACKGROUND: Minimally invasive endoscopic surgery has become an acceptable method for gynecologic indications for more than 20 years. We aimed to compare clinical and surgical outcomes between mini-laparoscopic surgery (MLS) and conventional laparoscopic surgery (CLS) for benign adnexal masses. As far as we know, no comparative study exists between these two minimal invasive procedures. METHODS: During the period between January 2014 and December 2016, a total number of 132 laparoscopic surgeries were performed for bening adnexal masses in our clinic. Seventy women underwent CLS and 62 women underwent MLS. Pathological results and operating time of procedures, estimated blood loss, preoperative and postoperative complications, patient scale and observer scale (POSAS) and length of hospital stay were recorded. RESULTS: There was no difference between the two groups regarding preoperative diagnosis, intraoperative surgical procedure performed, and length of hospital stay. The groups were compared in terms of postoperative pathological diagnosis using the Chi-square test, and there was a statistically significant difference between the two groups. Comparing the operation time and hematocrit change, there were statistically significant differences between the two groups. Both patient and observer PSOAS scar scores were better in MLS group (P < 0.05). CONCLUSIONS: Mini-laparoscopy can be safely and effectively used to perform benign adnexal mass surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".