Evaluation of Retinal Nerve Fiber Layer with Optic Nerve Tracking Optical Coherence Tomography in Thyroid-Associated Orbitopathy
Bibliographic record
Abstract
AIMS: To evaluate retinal nerve fiber layer (RNFL) thickness in eyes with Graves' orbitopathy (GO), in eyes with ocular hypertension (OHT) and in a control group of healthy eyes. METHODS: Observational, controlled cross-sectional study. We evaluated all patients with primary open-angle glaucoma (POAG) and all patients with GO and intraocular pressure >23 mm Hg in primary position examined from March 2006 to June 2007. Forty apparently healthy patients (80 eyes) were enrolled as a control group. Complete ophthalmic evaluation, visual field (VF) examination with the Humphrey Visual Field Analyzer and RNFL thickness measurement with optic nerve tracking optical coherence tomography (ONT-OCT; OCT/SLO, OTI, Toronto, Ont., Canada) were performed. RESULTS: Among 116 eyes with POAG [58 patients, 32 males, 26 females, mean age 62 (46-71) years], RNFL was reduced in 87 eyes (75%, p = 0.05) when compared with the control group, and a good correlation was found between RNFL thickness and VF abnormalities (Spearman's rho 0.822; p = 0.001). Among 60 eyes [30 patients, 12 males, 18 females, mean age 56 (50-69) years] with GO and OHT, nonglaucomatous diffuse abnormalities of the VF were detected in 44 eyes (73.3%, p = 0.03), while RNFL thinning was present in 14 eyes (9 patients, 23.3%, p = 0.03). No correlation was found between RNFL thickness and VF abnormalities (Spearman's rho 0.365; p = 0.02). No significant differences in RNFL pattern were present between the group with GO, OHT and RNFL thinning and the group with POAG. CONCLUSIONS: In patients with GO and OHT, evaluation of RNFL thickness with ONT-OCT may represent an objective diagnostic technique for detecting optic neuropathy.
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 | 0.001 | 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".