Procalcitonin levels in pregnancy: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: The utility of procalcitonin to identify obstetric sepsis is unknown. OBJECTIVE: To calculate the mean (range) procalcitonin in pregnancy among healthy women not in labor (group 1), healthy women in labor (group 2), and women with preterm prelabor rupture of membranes (PPROM) without clinical chorioamnionitis (group 3). SEARCH STRATEGY: NLM PubMed, Elsevier Embase, and Wiley Cochrane Central Register of Controlled Trials from inception to February 21, 2022. SELECTION CRITERIA: Ten or more pregnant women with procalcitonin reported at more than 20 weeks of pregnancy, with information on labor, PPROM, and infection. Exclusions were major medical comorbidities. DATA COLLECTION AND ANALYSIS: Each abstract and full-text review was independently reviewed by the same two authors. Quality was reviewed using the Newcastle-Ottawa Scale. A meta-analysis was performed using a random effects model. MAIN RESULTS: The systematic review included 25 studies: 10 (40%) of good quality and 15 (60%) of poor quality. The meta-analysis included 21 studies. Mean procalcitonin in group 1 was 0.092 ng/mL (range 0.036-0.049 ng/mL), in group 2 it was 0.130 ng/mL (range 0.049-0.259 ng/mL), and in group 3 it was 0.345 ng/mL (range 0.005-1.292 ng/mL). CONCLUSIONS: Among healthy pregnant women not in labor, procalcitonin levels are comparable to those in non-pregnant adults and may be useful in identifying infection. Procalcitonin levels in other groups overlap abnormal values of procalcitonin in non-pregnant adults, and may not discriminate infection among women in labor or with obstetric comorbidities. PROSPERO: CRD42020157376, registered 4/28/2020.
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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 | 0.018 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".