Ureteric Injury During Gynaecological Surgery - Lessons from 20 Cases in Canada.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Ureteric injury is a complication of gynaecological surgery that can cause significant morbidity for the patient and is a leading cause of litigation in many countries. OBJECTIVES: To determine patient characteristics, peri-operative circumstances and clinical and legal outcomes of ureteral injuries associated with gynaecological surgery. PATIENTS AND METHODS: This is a retrospective review of 20 cases of ureteric injury during benign gynaecological surgery. MAIN OUTCOME MEASURES: All cases were assessed for the following variables-patient characteristics, indications for surgery, injury, postoperative symptoms and presentation, and clinical and legal outcomes. RESULTS: Risk factors associated with ureteric injury included obesity, previous laparotomic pelvic surgery, pelvic adhesions, large pelvic masses and intra-operative bleeding. 70% (14/20) of ureteral injuries were diagnosed after discharge. 50% (10/20) of patients had a complicated post-operative course and 45% (9/20) of cases resulted in unfavourable legal outcomes (settlement or lost at trial) for the surgeon. The conduct of surgery and the failure to act in a timely fashion postoperatively were the most frequent reasons for adverse clinical and unfavourable litigation outcomes for the surgeon. CONCLUSIONS: Intra-operative surgical consultation and ureteral identification should be considered if there is concern for ureteral involvement in the surgical field. Ureteric injury may not constitute negligence if it is demonstrated that the surgeon provided reasonable care that would be expected during the peri-operative phases. WHAT IS NEW: This review identifies patient characteristics and peri-operative variables that correlate with poor clinical and legal outcomes after ureteric injury.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it