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Assessment of the DRCR Retina Network Approach to Management With Initial Observation for Eyes With Center-Involved Diabetic Macular Edema and Good Visual Acuity

2020· article· en· W3006783853 on OpenAlexaboutno aff
Adam R. Glassman, Carl W. Baker, Wesley T. Beaulieu, Neil M. Bressler, Omar S. Punjabi, Cynthia R. Stockdale, Charles C. Wykoff, Lee M. Jampol, Jennifer K. Sun

Bibliographic record

VenueJAMA Ophthalmology · 2020
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
FundersNational Eye InstituteNational Institutes of HealthAllerganApellis PharmaceuticalsNovartisRegeneron PharmaceuticalsNovo NordiskSanofiSamsungBayer
KeywordsAfliberceptMedicineInterquartile rangeVisual acuityOphthalmologyMacular edemaRandomized controlled trialDiabetic retinopathyLaser coagulationSurgeryDiabetes mellitusBevacizumab

Abstract

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Importance: Among eyes with center-involved diabetic macular edema (CI-DME) and good visual acuity (VA), randomized clinical trial results showed no difference in VA loss between initial observation plus aflibercept only if VA decreased, initial focal/grid laser plus aflibercept only if VA decreased, or prompt aflibercept. Understanding the initial observation approach is relevant to patient management. Objective: To assess the DRCR Retina Network protocol-defined approach and outcomes of initial observation with aflibercept only if VA worsened. Design, Setting, and Participants: This was a post hoc secondary analyses of a randomized clinical trial of the DRCR Retina Network Protocol V that included 91 US and Canadian sites from November 2013 to September 2018. Participants were adults (n = 236) with type 1 or 2 diabetes, 1 study eye with CI-DME, and VA letter score at least 79 (Snellen equivalent, 20/25 or better) assigned to initial observation. Data were analyzed from March 2019 to November 2019. Interventions: Initial observation and follow-up with aflibercept only for VA loss of at least 10 letters from baseline at 1 visit or 5 to 9 letters at 2 consecutive visits. Follow-up occurred at 8 weeks and then every 16 weeks unless VA or optical coherence tomography central subfield thickness worsened. Main Outcomes and Measures: Whether individuals received aflibercept. Results: Among 236 eyes in 236 individuals (149 [63%] male; median age, 60 years [interquartile range, 53-67 years]) randomly assigned to initial observation, 80 (34%) were treated with aflibercept during 2 years of follow-up. At 2 years, the median VA letter score was 86.0 (interquartile range, 89.0-81.0; median Snellen equivalent, 20/20 [20/16-20/25]). Receipt of aflibercept was more likely in eyes with baseline central subfield thickness at least 300 μm (Zeiss-Stratus equivalent) vs less than 300 μm (45% vs 26%; hazard ratio [HR], 1.98 [95% CI, 1.26-3.13], continuous P = .005), moderately severe nonproliferative diabetic retinopathy (Early Treatment Diabetic Retinopathy Study retinopathy severity level 47) and above vs moderate nonproliferative diabetic retinopathy (retinopathy severity level 43) and below (51% vs 27%; HR, 2.22 [95% CI, 1.42-3.47], ordinal P < .001), and among participants whose nonstudy eye received DME treatment within 4 months of randomization vs not (52% vs 25%; HR, 2.55 [95% CI, 1.64-3.99], P < .001). Conclusions and Relevance: Most eyes managed with initial observation plus aflibercept only if VA worsened maintained good vision at 2 years and did not require aflibercept for VA loss. However, the eyes in the trial were approximately twice as likely to receive aflibercept for VA loss if they had greater baseline central subfield thickness, worse diabetic retinopathy severity level, or a nonstudy eye receiving treatment for DME. Trial Registration: ClinicalTrials.gov Identifier: NCT01909791.

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)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.323
Teacher spread0.286 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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

Citations48
Published2020
Admission routes1
Has abstractyes

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