ArticleFamily medicine and community health2021
Primer on binary logistic regression.
Article in Family medicine and community health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled 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.
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
73 citing papers in PubMed, 1 synthesis or guideline pooled it, 171 citations in OpenAlex.
- Pooled it
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- Sex-specific clinical profiles and long-term outcomes in adolescents and young people at clinical high risk for psychosis: Findings from the PARMS program.European child & adolescent psychiatry · 2026Article
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- Subjective language disturbances in young patients at ultra-high risk for psychosis (UHR): what relevance for clinical prognosis? A 2-year follow-up study.European archives of psychiatry and clinical neuroscience · 2026Article
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- Exposure to Antipsychotics in Youths at Clinical High Risk for Psychosis: Low VS High Doses and Their Relevance for Clinical Outcomes.Early intervention in psychiatry · 2026Article
- Quality of life after preoperative home-based exercise training in patients with lung cancer: a secondary analysis.Journal of thoracic disease · 2026Article
- Trends in prenatal alcohol use from 2001 to 2017 in the Northern Territory, Australia: a joinpoint regression analysis.BMC pregnancy and childbirth · 2026Article
- Describing the Profile of Individuals at Heightened Risk for Cardiometabolic Multimorbidity: A Secondary Analysis of the Canadian Longitudinal Study on Aging Data.American journal of health promotion : AJHP · 2026Observational
- Development and validation of age-specific predictive models on the risk of post-acute mortality within 1 year of COVID-19 infection.QJM : monthly journal of the Association of Physicians · 2026Article
- Rotator Cuff-Related Shoulder Pain: A Survey of Current Physiotherapy Practice in Cyprus.Clinics and practice · 2026Article
- Risk assessment of subclinical mastitis in Holstein cows in Jiangsu Province of China.Frontiers in veterinary science · 2026Article
- Care Aides Compassion Fatigue, Burnout, and Compassion Satisfaction Related to Long-Term Care (LTC) Working Environment.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2026Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Family medicine has traditionally prioritised patient care over research. However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. The model produces ORs, which suggest increased, decreased or no change in odds of being in one category of the outcome with an increase in the value of the predictor. Model significance quantifies whether the model is better than the baseline value (ie, the percentage of people with the outcome) at explaining or predicting whether the observed cases in the data set have the outcome. One model fit measure is the count- [Formula: see text], which is the percentage of observations where the model correctly predicted the outcome variable value. Related to the count- [Formula: see text] are model sensitivity-the percentage of those with the outcome who were correctly predicted to have the outcome-and specificity-the percentage of those without the outcome who were correctly predicted to not have the outcome. Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit.
Indexed as
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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.