ArticlePloS one2013
Predictability of persistent frequent attendance in primary care: a temporal and geographical validation study.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
What it found
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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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 citations in OpenAlex.
- Determinants of Frequent Attendance in Primary Care. A Systematic Review of Longitudinal Studies.Frontiers in medicine · 2021Pooled it
- Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature.BMC medical informatics and decision making · 2017Pooled it
- Development and external validation of a risk prediction tool for recurrent wheeze attacks in preschool children in England.ERJ open research · 2026Article
- Continuity of care as a predictor of ongoing frequent attendance in primary care: a retrospective cohort study.BJGP open · 2020Article
- Determinants related to gender differences in general practice utilization: Danish Diet, Cancer and Health Cohort.Scandinavian journal of primary health care · 2016Article
- Clinical characteristics of persistent frequent attenders in primary care: case-control study.Family practice · 2015Article
- How GPs can Recognize Persistent Frequent Attenders at Finnish Primary Health Care Using Electronic Patient Records.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFrequent attenders are patients who visit their general practitioner exceptionally frequently. Frequent attendance is usually transitory, but some frequent attenders become persistent. Clinically, prediction of persistent frequent attendance is useful to target treatment at underlying diseases or problems. Scientifically it is useful for the selection of high-risk populations for trials. We previously developed a model to predict which frequent attenders become persistent.
aimTo validate an existing prediction model for persistent frequent attendance that uses information solely from General Practitioners' electronic medical records.
methodsWe applied the existing model (N = 3,045, 2003-2005) to a later time frame (2009-2011) in the original derivation network (N = 4,032, temporal validation) and to patients of another network (SMILE; 2007-2009, N = 5,462, temporal and geographical validation). Model improvement was studied by adding three new predictors (presence of medically unexplained problems, prescriptions of psychoactive drugs and antibiotics). Finally, we derived a model on the three data sets combined (N = 12,539). We expressed discrimination using histograms of the predicted values and the concordance-statistic (c-statistic) and calibration using the calibration slope (1 = ideal) and Hosmer-Lemeshow tests.
resultsThe existing model (c-statistic 0.67) discriminated moderately with predicted values between 7.5 and 50 percent and c-statistics of 0.62 and 0.63, for validation in the original network and SMILE network, respectively. Calibration (0.99 originally) was better in SMILE than in the original network (slopes 0.84 and 0.65, respectively). Adding information on the three new predictors did not importantly improve the model (c-statistics 0.64 and 0.63, respectively). Performance of the model based on the combined data was similar (c-statistic 0.65).
conclusionThis external validation study showed that persistent frequent attenders can be prospectively identified moderately well using data solely from patients' electronic medical records.
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