Evidence map›Paper›PMID 34708256›Full record

ArticleArchives of gynecology and obstetrics2022

Prediction of successful trial of labor after cesarean among grand-multiparous women.

Gabriel Levin, Abraham Tsur, Lee Tenenbaum, Nizan Mor, Michal Zamir, Raanan Meyer

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In one paragraph

Article in Archives of gynecology and obstetrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 8 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Gabriel LevinDepartment of Gynecologic Oncology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, POB 12000, 91120, Jerusalem, Israel. levin.gaby@gmail.com.ORCID 0000-0003-1282-5379
Abraham TsurDepartment of Obstetrics and Gynecology, Chaim Sheba Medical Center, Ramat-Gan, Israel.
Lee TenenbaumFaculty of Medicine, Tel-Aviv-Hebrew University, Tel-Aviv, Israel.
Nizan MorFaculty of Medicine, Tel-Aviv-Hebrew University, Tel-Aviv, Israel.
Michal ZamirFaculty of Medicine, Tel-Aviv-Hebrew University, Tel-Aviv, Israel.
Raanan MeyerDepartment of Obstetrics and Gynecology, Chaim Sheba Medical Center, Ramat-Gan, Israel.
Tel Aviv University · ILHebrew University of Jerusalem · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo determine the factors associated with successful trial of labor after cesarean (TOLAC) among grand-multiparous (GMP) women.

methodsA retrospective cohort study was conducted, including all GMP women (≥ 5 deliveries) undergoing TOLAC during 3/2011 and 6/2020, delivering a singleton cephalic newborn. Factors associated with successful vaginal delivery were examined by multivariable analysis.

resultsOverall, 381/413 (92.2%) GMP succeeded TOLAC. Maternal characteristics did not differ between TOLAC success and TOLAC failure groups. Previous cesarean delivery characteristics did not differ between study groups. The median number of previous vaginal births after cesarean was 2 [interquartile range 1-4]. Gestational age at TOLAC was lower in the success group (mean 37

conclusionTOLAC among GMP has a very-high success rate. Shortening the duration of ruptured membranes is a modifiable factor that may be associated with increased TOLAC success rates.

Indexed as

Trial of LaborVaginal Birth after CesareanDelivery, ObstetricFemaleHumansInfant, NewbornParityPregnancyRetrospective StudiesCesarean deliveryGrand multiparousTrial of laborUterine ruptureVaginal birth

Identifiers

PMID34708256
OpenAlexW3210181001

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.