Optimal management of post-discharge postpartum hypertensive disorders of pregnancy: a quality improvement initiative
Bibliographic record
Abstract
Introduction: Postpartum hypertensive disorders of pregnancy occur in 2-5% of pregnancies. It is a major cause of urgent postpartum consultation and is associated with life-threatening complications. Our objective was to evaluate if local management of postpartum hypertensive disorders of pregnancy was congruent with expert recommendations. Methods: We conducted a quality improvement initiative through a retrospective single-centre cross-sectional study. All women over 18-year-old consulting emergently for hypertensive disorders of pregnancy in the first six weeks postpartum, from 2015 to 2020, were eligible. Results: We included 224 women. Optimal management of postpartum hypertensive disorders of pregnancy was observed in 65.0%. While diagnosis and laboratory work-up were excellent, adequate blood pressure surveillance and recommendations upon discharge of an outpatient postpartum episode (69.7%) did not meet expectations. Conclusion: Efforts should be targeted to improve discharge recommendations on optimal blood pressure surveillance after delivery for women at risk for hypertensive disorders of pregnancy and for postpartum hypertensive disorders of pregnancy in women treated as outpatients.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".