Retinal Thinning as a Marker of Disease Severity in Progressive Supranuclear Palsy
Bibliographic record
Abstract
OBJECTIVE: Progressive supranuclear palsy (PSP) involves a variety of visual symptoms that are thought to be partially caused by structural abnormalities of the retina. However, the relationship between retinal structural changes, disease severity, and intracranial alterations remains unknown. We investigated distinct retinal thinning patterns and their relationship with clinical severity and intracranial alterations in a PSP cohort. METHODS: We enrolled 19 patients with PSP (38 eyes) and 20 age-matched healthy controls (40 eyes). All of the participants underwent peripapillary and macular optical coherence tomography. Brain 11C-2β-carbomethoxy-3β-(4-fluorophenyl) tropane (11C-CFT) and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography imaging were also performed in patients with PSP. We investigated the association between retinal thickness changes and clinical features, striatal dopamine transporter availability, and cerebral glucose metabolism. RESULTS: The peripapillary retinal nerve fiber layer (pRNFL) and macula were significantly thinner in patients with PSP than in controls. The thickness of the superior sector of the pRNFL demonstrated a significant negative relationship with the Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III and Hoehn and Yahr staging scale scores. A significant negative correlation was found between outer inferior macular thickness and disease duration. Outer temporal macular thickness was positively correlated with Montreal Cognitive Assessment scores. In PSP, lower outer temporal macular thickness was also positively correlated with decreased dopamine transporter binding in the caudate. CONCLUSION: The pRNFL and macular thinning may be candidate markers for monitoring disease severity. Additionally, macular thinning may be an in vivo indicator of nigrostriatal dopaminergic cell degeneration in PSP patients.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".