Evaluation of a clinical protocol for the management of fever in labor among pregnant women at term: A quality‐improvement study
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of a quality-improvement initiative designed to increase diagnostic accuracy and adequate management of clinical chorioamnionitis (CC) at a tertiary center. Chorioamnionitis occurs in 1%-13% of term pregnancies and increases maternal and neonatal peripartum complications; often over-diagnosed, it leads to unnecessary investigations and treatments. METHODS: This was an interrupted time-series study. In September 2017 two interventions were implemented: (1) staff training and (2) standardized clinical protocol for the management of fever in labor. All singleton term pregnancies were included. CC cases were reviewed in the pre-intervention (2015-2016, n = 179) and post-intervention (2017-2018, n = 142) groups. CC criteria based on the American College of Obstetricians and Gynecologists guidelines, antibiotics, maternal and neonatal outcomes, and pathology were compared. A cost-consequence analysis was performed. RESULTS: Incidence of CC decreased from 8.2 to 5.6 per 10 person-year (P < 0.001). This was associated with a significant increase in diagnostic accuracy from 15.7% to 73.2% (P < 0.001). Weight-adjusted tobramycin dosage improved from 8.8% to 69.1% (P < 0.001). Maternal length of hospitalization and readmissions decreased significantly, without affecting neonatal sepsis rate. Interventions decreased yearly hospital costs associated with CC by 23.4%. CONCLUSION: Standardizing the management of fever in labor significantly increased the diagnostic accuracy of CC and decreased the misuse of antibiotics in term pregnancies. CC costs decreased by 23.4%.
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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 | 0.300 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".