Morbidity and predictors of delayed recognition of iatrogenic ureteric injuries
Bibliographic record
Abstract
INTRODUCTION: Although intraoperative iatrogenic ureteric injuries (IUI) are rare, significant consequences can occur if they are unrecognized at the time. The focus of our study is to characterize the associated morbidity and identify predictors of delayed recognition of IUI. METHODS: Sunnybrook Health Sciences Centre Research Ethics Board approved the study. Patients with a diagnosis of IUI between 2002 and 2020 were identified through an institutional electronic medical record system. Data pertaining to the demographic characteristics, diagnosis, and management of IUI, as well as overall outcomes were collected retrospectively. RESULTS: Of the 103 patients identified, 83% were female, 52% had previous abdominal surgery, and 18% had previous radiation. The median age was 67 (range 21-88) years. Twenty percent were not recognized at the time of surgery. Although delayed recognition was not a significant predictor for poor outcome after subsequent repair (i.e., hydronephrosis, ureteric stricture/obstruction), it was associated with substantial morbidity to the patient (i.e., additional procedures) and increased cost to the healthcare system (i.e., longer hospital stay, re-admission to hospital). Patients who underwent laparoscopic surgery had an 11 times more likely chance of having an unrecognized IUI as compared to those who underwent open surgery (odds ratio 11.515, p=0.0001). CONCLUSIONS: Delayed recognition of IUI may be associated with considerable adverse effects. In this retrospective case series, we identified laparoscopic surgery as a significant predictor for delayed recognition of IUI. This information underscores the need for future studies to facilitate intraoperative identification of ureteric injuries, particularly during laparoscopic procedures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".