Evidence map›Paper›PMID 33845015›Full record

ArticleAmerican journal of ophthalmology2021

Classification Criteria for Cytomegalovirus Retinitis.

Standardization of Uveitis Nomenclature (SUN) Working Group

Registry-linked trialOpen access · greenAbstract read
In one paragraph

Article in American journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07679412 (An Exploratory Clinical Study of Autologous LB-DTK-CMV in Patients With Antiviral-Resistant and Refractory Cytomegalovirus Retinitis.), which is not on this map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
6.9field-weighted citation impact, top 2% of its field
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.

NCT07679412 narecruitingnot on this mapstarted 2026, after this paper: background citation

An Exploratory Clinical Study of Autologous LB-DTK-CMV in Patients With Antiviral-Resistant and Refractory Cytomegalovirus Retinitis.

TypeinterventionalSponsorLucasBioRan2026 to 2027Enrolled5ConditionsCMV RetinitisArmsLB-DTK-CMV
3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 66 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. A machine learning model for accurate prediction of CMV end-organ disease.BMC medical informatics and decision making · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Standardization of Uveitis Nomenclature (SUN) Working Group

Funding

DEVELOPING CLASSIFICATION CRITERIA FOR THE UVEITIDESR01EY026593 · NEI · JOHNS HOPKINS UNIVERSITY · PI JABS, DOUGLAS A · 2016 to 2019
$1.5M
NEI NIH HHS R01 EY026593
6 · The paper itself

Abstract

purposeThe purpose of this study was to determine classification criteria for cytomegalovirus (CMV) retinitis.

designMachine learning of cases with CMV retinitis and 4 other infectious posterior/ panuveitides.

methodsCases of infectious posterior/panuveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on diagnosis using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the infectious posterior/panuveitides. The resulting criteria were evaluated in the validation set.

resultsA total of 803 cases of infectious posterior/panuveitides, including 211 cases of CMV retinitis, were evaluated by machine learning. Key criteria for CMV retinitis included: 1) necrotizing retinitis with indistinct borders due to numerous small satellites; 2) evidence of immune compromise; and either 3) a characteristic clinical appearance, or 4) positive polymerase chain reaction assay results for CMV from an intraocular specimen. Characteristic appearances for CMV retinitis included: 1) wedge-shaped area of retinitis; 2) hemorrhagic retinitis; or 3) granular retinitis. Overall accuracy for infectious posterior/panuveitides was 92.1% in the training set and 93.3% (95% confidence interval: 88.2-96.3) in the validation set. The misclassification rates for CMV retinitis were 6.9% in the training set and 6.3% in the validation set.

conclusionsThe criteria for CMV retinitis had a low misclassification rate and appeared to perform sufficiently well for use in clinical and translational research.

Indexed as

Machine LearningAdultCytomegalovirusCytomegalovirus RetinitisDNA, ViralEye Infections, ViralFemaleHumansMaleMiddle AgedDNA, Viral

Identifiers

PMID33845015
PMCPMC8594755
OpenAlexW3153520094

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.