ArticleObstetrical & gynecological survey2017
The Power and Pitfalls of Big Data Research in Obstetrics and Gynecology: A Consumer's Guide.
Article in Obstetrical & gynecological survey, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 20 citations in OpenAlex.
- The Carolina hysterectomy cohort (CHC): a novel case series of reproductive-aged hysterectomy patients across 10 hospitals in the US south.BMC women's health · 2023Article
- General health status of Dutch elderly receiving implant-retained overdentures: A 9-year big data cross-sectional study.Clinical implant dentistry and related research · 2021Article
- Protocol for the development and validation of a risk prediction model for stillbirths from 35 weeks gestation in Australia.Diagnostic and prognostic research · 2020Article
- Revisiting the management of term breech presentation: a proposal for overcoming some of the controversies.BMC pregnancy and childbirth · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
importanceResearch in obstetrics and gynecology (OB/GYN) increasingly relies on "big data" and observational study designs. There is a gap in practitioner-relevant guides to interpret and critique such research.
objectiveThis guide is an introduction to interpreting research using observational data and provides explanations and context for related terminology. In addition, it serves as a guide for critiquing OB/GYN studies that use observational data by outlining how to assess common pitfalls of experimental and observational study designs. Lastly, the piece provides a compendium of observational data resources commonly used within OB/GYN research. EVIDENCE ACQUISITION: Review of literature was conducted for the collection of definitions and examples of terminology related to observational data research. Data resources were collected via Web search and researcher recommendations. Next, each data resource was reviewed and analyzed for content and accessibility. Contents of data resources were organized into summary tables and matched to relevant literature examples.
resultsWe identified 26 observational data resources frequently used in secondary analysis for OB/GYN research. Cost, accessibility considerations for software/hardware capabilities, and contents of each data resource varied substantially. CONCLUSIONS AND RELEVANCE: Observational data sources can provide researchers with a variety of options in tackling their research questions related to OB/GYN practice, patient health outcomes, trends in utilization of medications/procedures, or prevalence estimates of disease states. Insurance claims data resources are useful for population-level prevalence estimates and utilization trends, whereas electronic health record-derived data and patient survey data may be more useful for exploring patient behaviors and trends in practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.