Inner retinal thinning as a biomarker for cognitive impairment in de novo Parkinson’s disease
Bibliographic record
Abstract
We investigated the association between retinal changes measured using optical coherence tomography (OCT) and diverse clinical grading scales in patients with Parkinson's disease (PD). Seventy-four eyes of 74 patients with de novo PD and 53 eyes of age-matched control subjects were included. The thickness of the peripapillary retinal nerve fiber layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGCIPL) were measured. We analyzed the correlations between the clinical PD grading scales and OCT parameters, and between the OCT parameters and volumetric data in the cerebral cortical and subcortical structures. The area under the receiver operating characteristic curve (AUC) was calculated for diagnosing cognitive impairment in patients with PD. Statistically significant reductions in the thickness of average, temporal, and inferior pRNFL and overall mGCIPL were observed in patients with PD. The Montreal Cognitive Assessment score was significantly associated with mGCIPL thinning. The AUC of the mGCIPL parameters for diagnosing cognitive impairment in patients with PD ranged from 0.651 to 0.760. Moreover, thinning of the mGCIPL was significantly associated with the volumetric parameters of associated brain structures. Our findings highlight the clinical implications of OCT measurements as a potential biomarker for early detection of cognitive impairment in patients with PD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".