Outer retinal layer thickness in patients with glaucoma with horizontal hemifield visual field defects
Bibliographic record
Abstract
BACKGROUND/AIMS: To determine the effect of glaucoma on outer retinal layer thickness in eyes with horizontal hemifield visual field (VF) defects. METHODS: We conducted a cross-sectional study in glaucomatous eyes with repeatable (in three or examinations) horizontal hemifield VF (programme 24-2) defect defined as: all five nasal VF locations immediately either above or below the horizontal midline abnormal in the pattern deviation plot with p<0.5%; no mirror-image adjacent 5 VF locations abnormal in the pattern deviation plot and no non-edge VF locations in the non-affected hemifield abnormal in the pattern deviation plot with p<1%. We used optical coherence tomography to measure thickness of each retinal layer in the temporal macula (12° horizontally and 24° vertically) and computed the absolute (µm) and relative (%) intraindividual asymmetry between the perimetrically normal and abnormal hemimacula. RESULTS: We included 10 eyes of 8 patients with median age of 67 years and median VF mean deviation of -8.85 dB. The nerve fibre, ganglion cell and inner plexiform layers were significantly thinner in the perimetrically abnormal hemimacula (median asymmetry of -6.4, -11.5 and -3.8 µm, (corresponding to -27.7, -40.5 and -15.7 %), respectively, all p≤0.01). The inner nuclear layer was slightly thicker in the perimetrically abnormal hemimacula (median asymmetry of 1.3 µm (5.0 %), p=0.01). The outer plexiform, outer nuclear and photoreceptor layers asymmetry values were negligible. CONCLUSION: Our study showed no evidence that glaucoma has an effect on the outer retinal layer thickness. In contrast, a large impact was observed in inner layer thickness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.002 | 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".