ArticleBMJ open2019
Development of a prediction model to aid primary care physicians in early identification of women at high risk of developing endometriosis: cross-sectional study.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 13 citations in OpenAlex.
- MicroRNome analysis generates a blood-based signature for endometriosis.Scientific reports · 2022Trial
- Development of an early prediction model for endometriosis risk: the simplified adolescent factors for endometriosis (SAFE) score.EClinicalMedicine · 2026Article
- Screening for endometriosis: A scoping review of screening measures that could support early diagnosis.BMC women's health · 2025Article
- An accessible, non-invasive tool for endometriosis diagnosis reveals an association between age at symptom onset and endometriosis symptom prevalence.Journal of endometriosis and pelvic pain disorders · 2024Article
- Endometriosis Predictive Models Based on Self-Assessment Questionnaire, Evidence from Clinical Examination or Imaging Findings: A Narrative Review.Journal of clinical medicine · 2024Review
- Machine learning algorithms as new screening approach for patients with endometriosis.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo identify predictors of disease among a few factors commonly associated with endometriosis and if successful, to combine these to develop a prediction model to aid primary care physicians in early identification of women at high risk of developing endometriosis.
designCross-sectional anonymous postal questionnaire study.
settingWomen aged 18-45 years recruited from the Norwegian Endometriosis Association and a random sample of women residing in Oslo, Norway.
participants157 women with and 156 women without endometriosis.
main outcome measuresLogistic and least absolute shrinkage and selection operator (LASSO) regression analyses were performed with endometriosis as dependent variable. Predictors were identified and combined to develop a prediction model. The predictive ability of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and positive predictive values (PPVs) and negative predictive values (NPVs). To take into account the likelihood of skewed representativeness of the patient sample towards high symptom burden, we considered the hypothetical prevalences of endometriosis in the general population 0.1%, 0.5%, 1% and 2%.
resultsThe predictors
conclusionsExternal validation is needed before model implementation. Meanwhile, endometriosis should be considered a differential diagnosis in women with frequent absenteeism from school or work due to painful menstruations and positive family history of endometriosis.
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.