A Retrospective Comparative Analysis of 2D Versus 3D Laparoscopy in Total Laparoscopic Hysterectomy for Large Uteri (≥ 500g).
Bibliographic record
Abstract
STUDY OBJECTIVE: To evaluate the outcomes of total laparoscopic hysterectomy using 3D vision in comparison with 2D vision in women with large uteri (≥500g). DESIGN: Retrospective analytical study Design Classification: Canadian Task Force II-1 Setting: Tertiary referral center for advanced gynecological surgery. PATIENTS: Five hundred forty six women who underwent total laparoscopic hysterectomy over a period of 13 years were studied: 301 under 2D vision and 245 under 3D vision. INTERVENTIONS: Total laparoscopic hysterectomy Measurements: Surgical time, blood loss and complications were recorded for every case in both groups. MAIN RESULTS: The duration of surgery for hysterectomy in the 3D laparoscopy group (88.01?36.95 min) was significantly shorter than that in the 2D group (112.61?42.59 min, p=.0001). Blood loss in the 500-1000g group was significantly less in the 3D group (p=.005). The total complication rates for 3D surgery (3.37 %) and 2D surgery (6.64%) were comparable (p=.25). CONCLUSION: Three-dimensional laparoscopy provides stereoscopic vision and increases precision and safety. The availability of depth perception adds to the ease of surgery, especially in cases of large uteri, leading to reductions in both the duration of surgery and blood loss, which improves patient outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".