Assessment of Central Retinal Function in Patients with Advanced Retinitis Pigmentosa
Bibliographic record
Abstract
PURPOSE: To assess central retinal function in patients with advanced retinitis pigmentosa (RP) using the multifocal (mf)ERG and static perimetry. METHODS: Patients with RP; a nonrecordable, full-field (ff)ERG; and visual acuity (VA) of </= 1.0 logMAR were included. All patients underwent mfERG testing (103 hexagons, and 2.67 and 5.33 cd . s . m(-2) flash intensities) and static perimetry (103 corresponding areas) in the better eye. First-order kernel mfERGs were analyzed for total noise, signal-to-noise ratio, response amplitude, and implicit time. The number of areas with recordable mfERG responses were counted and compared with visual field (VF) sensitivity. RESULTS: Twenty-nine patients aged 16 to 68 years with a VA of 0.02 to 1.0 logMAR and a kinetic VF of 10 degrees to 60 degrees in diameter were included. mfERGs were successfully performed in 22 of 29 patients. Responses were detected in at least one stimulated area in 22 of 22 patients, with an overall response detection of 9.8% in all stimulated areas and no difference between flash intensities. All responses were diminished severely in response density P1-N1, with normal P1 implicit time in 50% of the recordings. No predictive factors for recordable mfERG responses were identified. VF results were recorded reliably in 27 of 29 patients, with a 40% response detection rate. CONCLUSIONS: mfERG responses were recordable in at least one area in all successfully tested patients with advanced RP. Response detection and performance was significantly higher for static perimetry. Static perimetry may be a more sensitive primary outcome measure of central vision function than the mfERG in patients with advanced RP and nonrecordable ffERGs.
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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 | 0.000 | 0.001 |
| 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".