Qualitative evaluation of neuroretinal rim and retinal nerve fibre layer on optical coherence tomography to detect glaucomatous damage
Bibliographic record
Abstract
PURPOSE: To understand the added value of Bruch's membrane opening-minimum rim width (BMO-MRW) measurements to conventional circumpapillary retinal nerve fibre layer (cpRNFL) thickness measurements on optical coherence tomography (OCT) imaging for discriminating between perimetric glaucoma and healthy eyes, evaluated through a qualitative evaluation. METHODS: 384 healthy eyes and 188 glaucoma eyes were evaluated, and glaucoma eyes were categorised as perimetric (n=107) based on a history of ≥3 consecutive abnormal 24-2 visual field tests or suspected glaucoma if they did not (n=81). OCT-derived BMO-MRW and cpRNFL reports were qualitatively evaluated by two experienced graders in isolation at first, and then by using both reports combined. The diagnostic performance (sensitivity at 95% specificity, total and partial area under the receiver operating characteristic curve) of detecting perimetric glaucoma with each method were compared. RESULTS: All diagnostic performance measures for detecting perimetric glaucoma eyes were not significantly different when using either the cpRNFL or BMO-MRW reports alone compared with using both reports combined (p≥0.190), nor when comparing the use of each report in isolation (p≥0.500). CONCLUSIONS: Experienced graders exhibited no difference in discriminating between perimetric glaucoma and healthy eyes when using a cpRNFL report alone, the BMO-MRW report alone or the two reports combined. Therefore, either OCT imaging report of the neuroretinal tissue could be used effectively for detecting perimetric glaucoma, but further studies are needed to determine whether there are specific advantages of each method, or the combination of both, when evaluating eyes that have a greater degree of diagnostic uncertainty.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".