Postoperative Urinary Retention after Pelvic Organ Prolapse Surgery – Influence of Peri-operative Factors and Trial of Void Protocol
Bibliographic record
Abstract
Abstract PurposeTransient postoperative urinary retention (POUR) is common after pelvic floor surgery. We aimed to determine the association between peri-operative variables and POUR and to determine the number of voids required for post-void residuals (PVRs) to normalize postoperatively. MethodsWe conducted a retrospective cohort study of 992 patients undergoing pelvic floor surgery at a tertiary referral centre from January 2015-October 2017. Variables assessed included: age, BMI, ASA score, anaesthesia type, type of surgery, length of postoperative stay, surgeon, bladder protocol used, and number of PVRs required to “pass” the protocol. Results Significant risk factors for POUR included: placement of MUS during POP surgery, anterior repair and hysterectomy with concomitant sacrospinous vault suspension.A total of 25.1% were discharged requiring catheterization. Patients receiving a concomitant mid-urethral sling (MUS) were 2.2 (95% CI:1.6-2.9) and 2.3 (95% CI:1.8-3.1) times more likely to have elevated PVR after their second TOV and third TOV (P<0.0001), respectively, compared with those without concomitant MUS. Permitting a third TOV allowed an additional 10% of women to pass the voiding protocol before discharge. The median number of voids to pass protocol was 2. An ASA>2 and placement of MUS were associated with increasing number of voids needed to pass protocol. ConclusionsWhile many women passed protocol by the second void, using the 3rd void as a cut point to determine success would result in fewer women requiring catheterization after discharge. Prior to pelvic floor surgery, women should be counselled regarding POUR probability to allow for management of postoperative expectations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".