2023 WSES guidelines for the prevention, detection, and management of iatrogenic urinary tract injuries (IUTIs) during emergency digestive surgery
Bibliographic record
Abstract
Iatrogenic urinary tract injury (IUTI) is a severe complication of emergency digestive surgery. It can lead to increased postoperative morbidity and mortality and have a long-term impact on the quality of life. The reported incidence of IUTIs varies greatly among the studies, ranging from 0.3 to 1.5%. Given the high volume of emergency digestive surgery performed worldwide, there is a need for well-defined and effective strategies to prevent and manage IUTIs. Currently, there is a lack of consensus regarding the prevention, detection, and management of IUTIs in the emergency setting. The present guidelines, promoted by the World Society of Emergency Surgery (WSES), were developed following a systematic review of the literature and an international expert panel discussion. The primary aim of these WSES guidelines is to provide evidence-based recommendations to support clinicians and surgeons in the prevention, detection, and management of IUTIs during emergency digestive surgery. The following key aspects were considered: (1) effectiveness of preventive interventions for IUTIs during emergency digestive surgery; (2) intra-operative detection of IUTIs and appropriate management strategies; (3) postoperative detection of IUTIs and appropriate management strategies and timing; and (4) effectiveness of antibiotic therapy (including type and duration) in case of IUTIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".