OP71 PASSPORT: placenta accreta spectrum patient outcomes of resuscitation + anesthesia technique: an analysis of practice patterns at two large tertiary referral centres in North America
Bibliographic record
Abstract
Background and Aims Placenta accreta spectrum (PAS) denotes a range of conditions with pathologic infiltration of trophoblastic and villous tissue resulting in higher rates of adverse maternal outcomes (1).The primary aim of this study was to review the anesthetic management of PAS at our institution. Methods This review was performed in a large tertiary centre in North America with two major sites that facilitate 6,800 deliveries annually. We conducted a chart review of PAS cases at our institution from 2019–2024. Results Thirty-nine cases were included, and relevant perioperative information is included in table 1. Thirty-five patients received epidurals with the level ranging from T7-L5 to allow for a midline incision, exteriorization of the uterus and fundal extrication of the fetus. The most common epidural level administered was T10-T11 (8), followed by L3-L4 (6) and L2-L3 (5). The highest pain scores experienced in recovery were recorded (0–10). For those with an epidural at T10–11, the mean pain score was 3.88, 4.6 for L3–4 and 2.25 for L2-L3. The average time to breakthrough analgesia was 95 minutes, with 35% of patients not requiring breakthrough pain medications, and 56.76% of patients not requiring opioids in recovery. The most common complication intraoperatively was a bladder cystotomy (6), and post-operatively was an ileus (4). Nine patients returned to hospital within 90 days (23%), and five were re-admitted. Conclusions PAS at our institution was primarily managed with a general anesthetic, and epidural anesthesia for post-operative pain control. We hope to use this data to perform subgroup analysis and compare to other centres.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".