Enhanced Structure-Function Relationship in Glaucoma With an Anatomically and Geometrically Accurate Neuroretinal Rim Measurement
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it