Surgery for giant ovarian cysts in Guyana using single-port laparoscopy: a retrospective case series analysis
Bibliographic record
Abstract
Giant ovarian cysts (GOCs) have become less common in developed countries due to routine health screenings, but they remain prevalent in economically underdeveloped regions. Treatment options for GOCs depend on factors such as age, cyst characteristics, and pregnancy status. Minimally invasive single-port laparoscopic surgery has largely replaced traditional open surgery due to its aesthetic advantages and effectiveness. In this case series, six patients with giant ovarian cysts, including two pregnant women, underwent single-port laparoscopic surgery. The procedure was aimed to reduce scarring, ensure safety, and preserve fertility. Strict criteria were used to rule out malignancy and assess suitability for the minimally invasive approach. Five patients had successful surgeries with positive cosmetic outcomes, while one required conversion to a mini-laparotomy due to technical difficulties. Postoperative recovery was smooth, with patients expressing satisfaction with the aesthetic results. This study demonstrates that single-port laparoscopic surgery is a safe and effective method for treating large ovarian cysts, combining the advantages of traditional laparoscopy with enhanced cosmetic results. The approach is particularly beneficial for younger and pregnant patients, with careful timing of surgery recommended to minimize pregnancy-related risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".