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Record W1993483010 · doi:10.1167/iovs.12-11521

Comparative Analysis of Repeatability of Manual and Automated Choroidal Thickness Measurements in Nonneovascular Age-Related Macular Degeneration

2013· article· en· W1993483010 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInvestigative Ophthalmology & Visual Science · 2013
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of British ColumbiaPraxis Spinal Cord InstituteSimon Fraser University
FundersCanadian Institutes of Health ResearchCarl Zeiss Meditec AG
KeywordsDrusenRepeatabilityMacular degenerationIntraclass correlationReproducibilityOptical coherence tomographyOphthalmologyMedicineArtificial intelligenceNuclear medicineComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

PURPOSE: We compared the reproducibility and mutual agreement of the subfoveal choroidal thickness measurements by expert raters and an automated algorithm in enhanced depth imaging optical coherence tomography (EDI-OCT) images of eyes with nonneovascular age-related macular degeneration (AMD). METHODS: We recruited 44 patients with nonneovascular AMD and EDI-OCT images were acquired. Subfoveal choroidal thickness was measured manually by two expert raters and automatically by a graph-cut-based algorithm. Drusen area was measured using the automated software (version 6) of Cirrus SD-OCT. The manual and automated choroidal thickness measurements were compared in reproducibility, mutual agreement, and correlation with drusen area. RESULTS: The mean subfoveal choroidal thickness was 246 ± 63 μm for the first rater, 214 ± 68 for the second rater, and 209 ± 53 for the automated algorithm. Intraclass correlation coefficients (ICC) and 95% confidence intervals (CI) were 0.96 (CI 0.94-0.98) between the raters, 0.85 (CI 0.77-0.90) between the first rater and the automated algorithm, and 0.84 (CI 0.75-0.89) between the second rater and the automated algorithm. Repeat scan measurement ICCs were 0.91 (CI 0.86-0.94) for the first rater, 0.96 (CI 0.94-0.97) for the second rater, and 0.87 (CI 0.80-0.92) for the automated algorithm. Manual and automated measurements were correlated with drusen area. CONCLUSIONS: The automated algorithm generally yielded smaller choroidal thickness than the raters with a moderate level of agreement. However, its repeat scan measurement repeatability was comparable to that of the manual measurements. The mean difference between the raters indicated possible biases in different raters and rating sessions. The correlation of the automated measurements with the drusen area was comparable to that of the manual measurements. Automated subfoveal choroidal thickness measurement has potential use in clinical practice and clinical trials, with possibility for reduced time and labor cost.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.376
Teacher spread0.319 · 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