The impact of vaginal pH on outcomes of induction of labour: a meta-analysis of observational studies
Bibliographic record
Abstract
The present meta-analysis is designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 12. The study will be based in aggregated data that have been already published in the international literature. Patient consent and institutional review board approval were not retrieved as they are not required in this type of studies. Types of studies and patients The eligibility criteria for the inclusion of studies will be predetermined. Observational studies including cohort and case control studies will be considered as eligible for inclusion in the present systematic review, regardless of the definition of acidic pH. Randomized trials that will compare outcomes among patients with an acidified and non-acidified pH will be also considered, provided that they will provide data concerning the outcomes of interest. Case series, case reports, experimental studies and conference proceedings will be excluded from the present systematic review. Information sources and search methods We will search Medline (1966–2021), Scopus (2004–2021), Clinicaltrials.gov (2008–2021), EMBASE (1980-2021), Cochrane Central Register of Controlled Trials CENTRAL (1999-2021) and Google Scholar (2004-2021) databases in our primary search along with the reference lists of electronically retrieved full-text papers. The date of our last search will be set at January 15, 2021. Our search strategy will include the text words “pH; vaginal; acidic; misoprostol; dinoprostone; labour; cesarean section”. Studies will be selected in three consecutive stages. Following deduplication, the titles and abstracts of all electronic articles will be screened by two authors to assess their eligibility. The decision for inclusion of studies in the present meta-analysis will be taken after retrieving and reviewing the full version of articles that will be considered potentially eligible. Discrepancies that will arise in this latter stage will be resolved by consensus from all authors. Predefined outcomes Outcome measures will be predefined during the design of the present systematic review. Data extraction will be performed using a modified data form that is based in Cochrane`s data collection form for intervention reviews for RCTs and non-RCTs. Predetermined primary outcomes will include the impact of an acidic vaginal pH on vaginal delivery rates, on the interval from induction to onset of 1st and 2nd stage of labor, the interval to delivery and the risk for developing post-partum hemorrhage, intrapartum uterine hyperstimulation and need for admission of the neonate in the intensive care unit. Assessment of risk of bias The methodological quality of included studies will be assessed with the Newcastle Ottawa scale. Statistical analysis Statistical meta-analysis will be performed with RStudio using the meta function (RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL http://www.rstudio.com/). Statistical heterogeneity will not considered during the evaluation of the appropriate model (fixed effects or random effects) of statistical analysis as the considerable methodological heterogeneity of observational studies does not leave space for assumption of comparable effect sizes among studies included in the meta-analysis 15. Confidence intervals will be set at 95%. We will calculate pooled odds ratios (OR), mean differences (MD) and 95% confidence intervals (CI) with the Hartung-Knapp-Sidik-Jonkman instead of the traditional Dersimonian-Laird random effects model analysis (REM). Publication bias will be assessed only if at least 10 studies will be included in each analyzed index. Prediction intervals Prediction intervals (PI) will be also calculated, using the meta function in RStudio, to evaluate the estimated effect that is expected to be seen by future studies in the field. The estimation of prediction intervals takes into account the inter-study variation of the results and express the existing heterogeneity at the same scale as the examined outcome. Subgroup analysis Subgroup analysis based on the used prostaglandin (misoprostol or dinoprostone) to evaluate potential differences in the response of the two prostaglandins in the acidic vaginal pH.
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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 | 0.036 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.093 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".