Enhanced Structure-Function Relationship in Glaucoma With an Anatomically and Geometrically Accurate Neuroretinal Rim Measurement
Bibliographic record
Abstract
PURPOSE: To evaluate the structure-function relationship between disc margin-based rim area (DM-RA) obtained with confocal scanning laser tomography (CSLT), Bruch's membrane opening-based horizontal rim width (BMO-HRW), minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer thickness (RNFLT) obtained with spectral-domain optical coherence tomography (SD-OCT), and visual field sensitivity. METHODS: We examined 151 glaucoma patients with CSLT, SD-OCT, and standard automated perimetry on the same day. Optic nerve head (ONH) and RNFL with SD-OCT were acquired relative to a fixed coordinate system (acquired image frame [AIF]) and to the eye-specific fovea-BMO center (FoBMO) axis. Visual field locations were mapped to ONH and RNFL sectors with fixed Garway-Heath (VF(GH)) and patient-specific (VF(PS)) maps customized for various biometric parameters. RESULTS: Globally and sectorally, the structure-function relationships between DM-RA and VF(GH), BMO-HRW(AIF) and VF(GH), and BMO-HRW(FoBMO) and VF(PS) were equally weak. The R(2) for the relationship between DM-RA and VF(GH) ranged from 0.1% (inferonasal) to 11% (superotemporal) whereas that between BMO-HRW(AIF) and VF(GH) ranged from 0.1% (nasal) to 10% (superotemporal). Relatively stronger global and sectoral structure-function relationships with BMO-MRW(AIF) and with BMO-MRW(FoBMO) were obtained. The R(2) between BMO-MRW(AIF) and VF(GH) ranged from 5% (nasal) to 30% (superotemporal), whereas that between BMO-MRW(FoBMO) and VF(PS) ranged from 5% (nasal) to 25% (inferotemporal). The structure-function relationship with RNFLT was not significantly different from that with BMO-MRW, regardless of image acquisition method. CONCLUSIONS: The structure-function relationship was enhanced with BMO-MRW compared with the other neuroretinal rim measurements, due mainly to its geometrically accurate properties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".