Long-term fluctuation in short-wavelength automated perimetry in glaucoma suspects and glaucoma patients.
Bibliographic record
Abstract
PURPOSE: To determine the magnitude of the homogeneous, LF(Ho), and the heterogeneous, LF(He), components of the long-term fluctuation (LF) in glaucoma suspects and in stable primary open angle glaucoma (POAG) patients undergoing short-wavelength automated perimetry (SWAP) and to compare the magnitude of the SWAP LF components with those elicited by standard white-on-white (W-W) perimetry. METHODS: The sample comprised 33 glaucoma suspects and 17 patients with early-to-moderate stable POAG who underwent W-W perimetry and SWAP at each of six visits over a mean period of 12.75 months (SD, 2.29). The LF(Ho), LF(He), and error components of the long-term fluctuation were determined between the third and seventh visual field examinations. The intervening visual field examinations and the optic nerve head parameters, derived both by stereo observation and by the Heidelberg Retinal Tomograph, were used to confirm stability over the follow-up period. RESULTS: The LF(Ho) and LF(He) components were larger in the POAG patients than in the glaucoma suspects for both W-W perimetry and SWAP; the magnitude was independent of the depth of defect and of the short-term fluctuation. All three components of long-term fluctuation were greater for SWAP than for W-W perimetry, both in the glaucoma suspects and in the POAG patients. CONCLUSIONS: SWAP exhibits greater long-term fluctuation than white-on-white perimetry. The usefulness of SWAP will be limited if the extent of this variability is not overcome in future statistical procedures developed to detect progressive visual field loss.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".