Evidence map›Paper›PMID 41586033›Full record

ArticleJIMD reports2026

Drivers of Diagnostic Delay in Mitochondrial Disease: Missed Recognition of Canonical Features.

Rory J Tinker, Neil Jacob, Mohammad Ghouse Syed, Janhawi Kelkar, Colleen Donnelly, Ibrahim Elsharkawi, Jaya Ganesh, Bruce D Gelb, Vikas Pejaver, Tamas Kozicz and 1 more

Abstract read
In one paragraph

Article in JIMD reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Rory J TinkerDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.ORCID https://orcid.org/0000-0001-7093-8844
Neil JacobDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.
Mohammad Ghouse SyedInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai New York New York USA.
Janhawi KelkarDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.
Colleen DonnellyDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.
Ibrahim ElsharkawiDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.
Jaya GaneshDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.
Bruce D GelbMindich Child Health and Development Institute and Departments of Pediatrics and Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York New York USA.
Vikas PejaverInstitute for Genomic Health, Icahn School of Medicine at Mount Sinai New York New York USA.
Tamas KoziczDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.
Eva MoravaDepartment of Medical Genetics and Genomics Icahn School of Medicine at Mount Sinai New York New York USA.

Funding

Conduits: Mount Sinai Health System Translational Science HubUL1TR004419 · NCATS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rosalind J Wright · 2022 to 2026
$46.4M
T32 training grant in medical genetics.T32GM082773 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Laszlo Tamas Kozicz, Eva Morava-Kozicz · 2008 to 2026
$4.9M
COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
Integrative data science approaches for rare disease discovery in health recordsR00LM012992 · NLM · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI PEJAVER, VIKAS RAO · 2022 to 2024
$709k
NCATS NIH HHS UL1 TR004419NIGMS NIH HHS T32 GM082773NIH HHS S10 OD026880NIH HHS S10 OD030463NLM NIH HHS R00 LM012992
6 · The paper itself

Abstract

Diagnostic delay is common in mitochondrial disease, and its drivers remain unclear despite advances in molecular diagnostics. We retrospectively analyzed 61 individuals with molecularly confirmed mitochondrial disease at the Mount Sinai Mitochondrial Disease Clinic, diagnosed after 2016. Diagnostic delay was partitioned into intervals from symptom onset to clinical suspicion, and from suspicion to molecular diagnosis. Demographic, phenotypic, and genetic data were abstracted from health records, and Human Phenotype Ontology terms were compared before and after diagnosis using ClinPhen. Most delays occurred between symptom onset and clinical suspicion (mean 8.17 years) rather than after suspicion (mean 1.28 years), yielding a mean total delay of 8.22 years (median 3.0). Delay decreased sharply by year of birth (

Indexed as

diagnostic delayelectronic health records (EHR)genotype‐first diagnosishuman phenotype ontology (HPO)missed diagnostic opportunitiesmitochondrial diseasenatural language processing (NLP)phenotype recognition

Identifiers

PMID41586033
PMCPMC12827488

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Registered trials

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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.