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Analysis of Discrepancy Between Diagnostic Clinical Examination Findings and Corresponding Evaluation of Digital Images in the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity Study

2016· article· en· W2521130223 on OpenAlexaff
Graham E. Quinn, Anna L. Ells, Antonio Capone, G. Baker Hubbard, Ebenezer Daniel, P. Lloyd Hildebrand, Gui‐Shuang Ying

Bibliographic record

VenueJAMA Ophthalmology · 2016
Typearticle
Languageen
FieldMedicine
TopicRetinopathy of Prematurity Studies
Canadian institutionsUniversity of Calgary
FundersNational Eye Institute
KeywordsRetinopathy of prematurityMedicineGrading (engineering)BlindingEye examinationTelemedicineChildhood blindnessPhysical examinationReferralGestational ageOphthalmologyPediatricsSurgeryPregnancyHealth careFamily medicineRandomized controlled trialVisual acuity

Abstract

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IMPORTANCE: As effective treatments for potentially blinding retinopathy of prematurity (ROP) have been introduced, the importance of consistency in findings has increased, especially with the shift toward retinal imaging in infants at risk of ROP. OBJECTIVE: To characterize discrepancies in findings of ROP between digital retinal image grading and examination results from the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity study, conducted from May 2011 to October 2013. DESIGN, SETTING, AND PARTICIPANTS: A poststudy consensus review of images was conducted by 4 experts, who examined discrepancies in findings between image grades by trained nonphysician readers and physician examination results in infants with referral-warranted ROP (RW-ROP). Images were obtained from 13 North American neonatal intensive care units from eyes of infants with birth weights less than 1251 g. For discrepancy categories with more than 100 cases, 40 were randomly selected; in total, 188 image sets were reviewed. MAIN OUTCOMES AND MEASURES: Consensus evaluation of discrepant image and examination findings for RW-ROP components. RESULTS: Among 5350 image set pairs, there were 161 instances in which image grading did not detect RW-ROP noted on clinical examination (G-/E+) and 854 instances in which grading noted RW-ROP when the examination did not (G+/E-). Among the sample of G-/E+ cases, 18 of 32 reviews (56.3%) agreed with clinical examination findings that ROP was present in zone I and 18 of 40 (45.0%) agreed stage 3 ROP was present, but only 1 of 20 (5.0%) agreed plus disease was present. Among the sample of G+/E- cases, 36 of 40 reviews (90.0%) agreed with readers that zone I ROP was present, 23 of 40 (57.5%) agreed with readers that stage 3 ROP was present, and 4 of 16 (25.0%) agreed that plus disease was present. Based on the consensus review results of the sampled cases, we estimated that review would agree with clinical examination findings in 46.5% of the 161 G-/E+ cases (95% CI, 41.6-51.6) and agree with trained reader grading in 70.0% of the 854 G+/E- cases (95% CI, 67.3-72.8) for the presence of RW-ROP. CONCLUSIONS AND RELEVANCE: This report highlights limitations and strengths of both the remote evaluation of fundus images and bedside clinical examination of infants at risk for ROP. These findings highlight the need for standardized approaches as ROP telemedicine becomes more widespread.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.964
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.098
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.248
GPT teacher head0.457
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2016
Admission routes1
Has abstractyes

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