Urological Involvement in the Multidisciplinary Management of Placenta Accreta Spectrum in a Centralised, High-Volume Centre: A Retrospective Analysis
Bibliographic record
Abstract
Objectives Placenta accreta spectrum (PAS) significantly increases the complexity of childbirth and frequently involves urologic organs. Multidisciplinary team (MDT) care is paramount to ensure optimal outcomes. We aimed to evaluate urologic interventions in patients with PAS at a centralised, tertiary referral centre. Methods An analysis of a prospectively collected data set, consisting of all women presenting with PAS at our institution between November 2013 and June 2019. Patients who required urological intervention were identified, and perioperative details were retrieved. Results Forty-two cases of PAS were identified. The mean maternal age was 35 years, and mean gestational age at delivery was 34 weeks. Thirty-seven cases were managed electively, with 5 cases managed conservatively (no hysterectomy) and 5 requiring emergency management. Fifteen patients (36%) had suspected bladder invasion on MRI. A total of 36 patients (86%) had ureteric catheters inserted, 14 (33%) required bladder repair, and 2 had ureteric injuries (5%). Conclusions PAS frequently requires urological intervention to prevent and repair injury to the urinary bladder and ureter. PAS is a rare condition that is best managed in an MDT setting in a centralised, tertiary, high-volume centre with access to a variety of medical and surgical sub-specialities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".