ArticleHuman genomics2026
Addressing the diagnostic gap through deep phenotyping.
Article in Human genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Exploring needs and priorities in digital health management for rare disease patients and their caregivers: A mixed-methods study.PLOS digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundRare diseases remain a substantial challenge for healthcare systems worldwide, and 80% are attributable to genetic factors. Although exome sequencing (ES) and genome sequencing (GS) have become routine and widely accessible in our current clinical practice due to reduced costs and policy support, progress in systematically capturing deep and structured phenotypic data has lagged behind, limiting diagnostic accuracy. Integrating deep phenotyping with genomic analysis may help close the diagnostic gap. However, its value has not been comprehensively assessed in clinical practice, particularly in the absence of a systematic medical geneticist program in our current clinical settings.
methodsWe accessed and reviewed the clinical and sequencing data of patients with a rare or suspected genetic disorder assessed at our center between 2022 and 2024. Through detailed case vignettes, we evaluated how granular bedside data influenced the diagnostic outcomes. Expert consensus and literature review were then used to construct a continually updated Recommended Phenotypes Panel and to build a nine-domain framework termed iDREAMS (integrating Deep phenotyping with genetic analysis for Rare disease Evaluation And Management Strategies).
resultsDeep phenotyping uncovered diagnostic clues missed by standard assessments across all nine domains, guiding genome analysis, shortening the diagnostic odyssey, and refining counselling in numerous cases. The panel streamlined data capture for clinicians, improved multidisciplinary case discussions, and fed directly into the iDREAMS framework, offering a reproducible pathway from phenotype to genotype.
conclusionsSystematic deep phenotyping is pivotal for closing the “sequencing-analysis” gap in rare disease diagnostics. The iDREAMS framework provides a practical, scalable model that enhances diagnostic accuracy, lowers the entry barrier for resource-limited settings, and extends the reach of precision medicine to patients who had long remained undiagnosed.
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.