Bibliographic record
Abstract
PURPOSE OF REVIEW: Maintaining micturition in the perioperative period can be challenging because of its low profile, other competing clinical criteria, poorly defined diagnostic criteria, and varying management strategies. Postoperative urinary retention, the main complication of micturition difficulties, has clinical implications in terms of perioperative outcome such as delayed discharge, iatrogenic infection from catheterization with the potential risk of systemic infection, and possible long-term bladder dysfunction. Factors contributing to postoperative micturition problems are multifactorial and anesthesiologists should consider the strategies to minimize the incidence of postoperative urinary retention. RECENT FINDINGS: Several factors have been identified as increasing the risk of perioperative micturition difficulties including medical comorbidities, surgical type, anesthetic type, and within anesthetic type specific agents such as long-acting neuraxial opioids. Current literature indicates that long-term sequelae are unlikely, with bladder overdistension lasting less than 4 h. SUMMARY: Employing strategies aimed at minimizing the disruptions in bladder function can mitigate perioperative micturition problems and subsequent complications. This requires a multifactorial approach. We present identified risk factors, considerations for their modification, as well as a classification and management strategy that incorporates the literature to date.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".