Atrophy of the lateral geniculate nucleus in human glaucoma detected by magnetic resonance imaging
Bibliographic record
Abstract
AIM: To determine in vivo whether the lateral geniculate nucleus (LGN) undergoes atrophy in patients with glaucoma and vision loss compared with normal subjects. METHODS: Following institutional St Michael's Hospital Research Ethics Board approval, a prospective and masked neuroimaging study was conducted on glaucoma patients with visual-field defects affecting both eyes (n = 10) and age-matched controls (n = 8). Following informed consent, all subjects underwent 1.5-Tesla MRI. Coronal proton density magnetic resonance images of both LGNs were obtained, and LGN height measurements were measured by consensus by three neuroradiologists masked to the diagnosis. Glaucoma and control groups were compared using the t test. RESULTS: Both LGNs were identified and visualised by 1.5-Tesla MRI for every subject. Compared with controls, the mean LGN heights in glaucoma were decreased in right (4.09 (0.89) mm vs 4.74 (0.54) mm, p>0.05) and left LGNs (3.98 (0.57) mm vs 4.83 (0.95) mm; p = 0.033). The combined right and left LGN height in glaucoma was significantly decreased compared with controls (8.07 (1.06) mm vs 9.56 (0.86) mm; p = 0.005). CONCLUSION: In vivo MRI evidence of LGN degeneration in human glaucoma is consistent with ex vivo primate and human neuropathological studies. LGN atrophy may be a relevant biomarker of visual system injury and/or progression in some glaucoma patients.
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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 | 0.000 |
| 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".