ArticleNPJ digital medicine2024
Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed.
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Interpretable fine-tuned large language models facilitate making genetic test decisions for rare diseases.NPJ digital medicine · 2026Article
- Developing a phenotype risk score formedRxiv : the preprint server for health sciences · 2026Article
- Genomics in Health and Biomedicine.Advances in experimental medicine and biology · 2026Review
- Genetics services in Latin America: a descriptive study of availability and utilization of genetics in healthcare.Journal of community genetics · 2025Article
- PRICE: a personalized recursive intelligent cost effectiveness analysis framework for rare disease diagnosis.BMC medical informatics and decision making · 2025Article
- Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.Journal of clinical medicine · 2025Review
Corrections and comments
- Update of
Authors and funding
12 authors.
Funding
Abstract
Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.
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.