Evaluation of the Structure-Function Relationship in Glaucoma
Bibliographic record
Abstract
PURPOSE: While glaucoma is evaluated on the basis of structural and functional test results, the spatial relationship between structure and function is not well defined. This study produces a topographical map to demonstrate how sectors of the optic nerve head (ONH) are related to locations in the visual field, using empiric cross-sectional patient data. METHODS: One hundred nine subjects with healthy eyes and 166 subjects with diagnosed or suspected glaucoma (one test per patient) were evaluated using a retina tomograph and white-on-white standard automated perimetry (SAP). The tomograph ONH images were divided into 36 sectors; and the sector rim areas normalized to account for changes in the total rim area. These were then correlated with SAP thresholds. For each visual field location, a map was produced indicating the strength of correlation between the normalized sector rim areas and thresholds. RESULTS: The highest correlation between a sector's normalized rim area and a SAP location's sensitivity was 0.520. Twenty-seven of the 52 non-blind spot SAP locations exhibited a correlation greater than 0.2 with at least one ONH sector. Locations in the superior hemifield were usually best correlated with the polar inferior temporal sectors of the ONH; locations in the inferior hemifield were usually best correlated with the polar superior temporal sectors of the ONH. CONCLUSIONS: A map relating regions of the ONH to SAP test locations has been produced. This map may be useful in elucidating the structure-function relationship, particularly for cases of localized glaucomatous loss.
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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 | 0.001 |
| 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) | 0.002 | 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".