Case report and surgical video presentation: Combined laparoscopic and cystoscopic partial bladder cystectomy for excision of deeply infiltrating endometriosis
Bibliographic record
Abstract
INTRODUCTION: Whilst endometriosis is a relatively common condition, deeply infiltrating endometriosis (DIE) of the bladder is less so, and when medical treatment fails, surgical management is an effective option. We present a case report and surgical video of a patient undergoing combined laparoscopic and cystoscopic excision of deeply infiltrating endometriosis of the bladder. DESIGN: Case report (Canadian Task Force Classification III) and step-by-step explanation of the surgery using video. Exemption was granted from the local institutional review board. PRESENTATION OF CASE: We present a case report and surgical video of a 36-year-old nulliparous patient presenting with a 12-month history of sudden onset cyclical dysuria and haematuria. Imaging demonstrated a deeply infiltrating endometriotic nodule involving the bladder. The patient underwent a combined laparoscopic and cystoscopic excision of deeply infiltrating endometriosis of the bladder. The procedure was uneventful and the patient progressed to a full recovery. DISCUSSION: DIE is a highly invasive form of endometriosis which is defined arbitrarily as endometriosis infiltrating beneath the peritoneum by 5mm or greater. When medical therapy is declined or fails, surgical excision by partial cystectomy would appear to be the most effective management option. A combination of cystoscopy and laparoscopy has been shown to be a safe and feasible procedure, with a low rate of complications. It represents the ideal way by which to identify the resection limits for complete excision of the lesion, and allows for optimal repair of the bladder defect. CONCLUSION: Combined laparoscopic and cystoscopic partial cystectomy for excision of deeply infiltrating bladder endometriosis is a safe and feasible procedure in our institution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".