ArticleFrontiers in medicine2026
A decision tree-based algorithm for structured risk stratification of rare rheumatic diseases in a tertiary referral setting.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Rare inflammatory rheumatic diseases are often characterized by heterogeneous, multisystemic symptom patterns, complicating early diagnostic differentiation. This study aimed to model a structured, symptom- and laboratory-based decision approach for risk stratification of rheumatologic diagnoses in patients referred to a tertiary Center for Rare Diseases (CRD) with unclear systemic complaints. Methods: We conducted a retrospective cross-sectional analysis of 173 patients evaluated at a CRD. Patients were classified into rheumatologic (RHEUMA) and non-rheumatologic (OTHER) diagnostic groups based on final diagnostic outcomes. A standardized questionnaire capturing 52 symptoms was aggregated into domain-specific and composite scores. Laboratory data were summarized into predefined indices. Group differences were analyzed descriptively using inferential statistics. A decision tree model based on Chi-Square Automatic Interaction Detection (CHAID) was constructed to support structured risk stratification. Results: A confirmed rheumatologic diagnosis was established in 52.0% of patients. Symptom and comorbidity patterns were broadly similar across diagnostic groups, with fatigue and generalized pain being the most prevalent complaints. The RHEUMA group showed significantly higher scores in selected symptom domains and in an immunoserological laboratory index (all Conclusion: The proposed decision tree model provides a transparent framework for structured risk stratification in a highly preselected tertiary referral population. Given the monocentric, retrospective, and exploratory study design, the findings should be interpreted as hypothesis-generating. External validation in independent cohorts is required to assess model generalizability, calibration, and robustness before clinical implementation can be considered. Within these constraints, the approach illustrates the potential of transparent, rule-based symptom aggregation to support structured clinical reasoning and prioritization in complex multisystem presentations.
Indexed as
Identifiers
What Socratic 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 Socratic graph.