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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".