The OSCAR-IB Consensus Criteria for Retinal OCT Quality Assessment
Bibliographic record
Abstract
BACKGROUND: Retinal optical coherence tomography (OCT) is an imaging biomarker for neurodegeneration in multiple sclerosis (MS). In order to become validated as an outcome measure in multicenter studies, reliable quality control (QC) criteria with high inter-rater agreement are required. METHODS/PRINCIPAL FINDINGS: A prospective multicentre study on developing consensus QC criteria for retinal OCT in MS: (1) a literature review on OCT QC criteria; (2) application of these QC criteria to a training set of 101 retinal OCT scans from patients with MS; (3) kappa statistics for inter-rater agreement; (4) identification reasons for inter-rater disagreement; (5) development of new consensus QC criteria; (6) testing of the new QC criteria on the training set and (7) prospective validation on a new set of 159 OCT scans from patients with MS. The inter-rater agreement for acceptable scans among OCT readers (n = 3) was moderate (kappa 0·45) based on the non-validated QC criteria which were entirely based on the ophthalmological literature. A new set of QC criteria was developed based on recognition of: (O) obvious problems, (S) poor signal strength, (C) centration of scan, (A) algorithm failure, (R) retinal pathology other than MS related, (I) illumination and (B) beam placement. Adhering to these OSCAR-IB QC criteria increased the inter-rater agreement to kappa from moderate to substantial (0.61 training set and 0.61 prospective validation). CONCLUSIONS: This study presents the first validated consensus QC criteria for retinal OCT reading in MS. The high inter-rater agreement suggests the OSCAR-IB QC criteria to be considered in the context of multicentre studies and trials in MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.016 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".