Safer outcomes for placenta accreta spectrum disorders: A decade of quality improvement
Bibliographic record
Abstract
OBJECTIVE: To describe the evolution and evaluation of protocol-based multidisciplinary quality improvement (QI) in women undergoing cesarean hysterectomy for radiologically suspected and pathologically confirmed placenta accreta spectrum (PAS) disorders. METHODS: A single-center, retrospective cohort study was conducted of all patients undergoing cesarean hysterectomy for PAS disorders between March 2009 and June 2018. Two distinct periods were defined to compare outcomes: 2009-2011 (initial period) and 2017-2018 (current period). Primary outcomes included blood loss and administration of blood products. Secondary outcomes included perioperative levels of hemoglobin, adverse events and complications, time to mobilization, and length of hospitalization. RESULTS: Among the 105 consecutive patients identified, there were 26 in the initial period and 32 in the current period. With the implementation of all QI care bundles, median estimated surgical blood loss halved from 2000 ml in the initial period to 1000 ml in the current period, and fewer patients required allogenic blood transfusion (61.5% vs 25%). Patients in the current period demonstrated improved postoperative levels of hemoglobin compared to those in the initial period (101 g/L vs 89 g/L) and had a shorter median postoperative hospital stay (3 days vs 5 days). CONCLUSION: These results support the implementation of a multifaceted QI and patient care initiative for women with PAS disorders.
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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 | 0.028 | 0.054 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".