Fixation Duration and Pupil Size as Diagnostic Tools in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Visual and oculomotor problems are very common in Parkinson's disease (PD) and by using eye-tracking such problems could be characterized in more detail. However, eye-tracking is not part of the routine clinical investigation of parkinsonism. OBJECTIVE: To evaluate gaze stability and pupil size in stable light conditions, as well as eye movements during sustained fixation in a population of PD patients and healthy controls (HC). METHODS: In total, 50 PD patients (66% males) with unilateral to mild-to-moderate disease (Hoehn & Yahr 1-3, Schwab and England 70-90%) and 43 HC (37% males) were included in the study. Eye movements were recorded with Tobii Pro Spectrum, a screen-based eye tracker with a sampling rate of 1200 Hz. Logistic regression analysis was applied to investigate the strength of association of eye-movement measures with diagnosis. RESULTS: Median pupil size (OR 0.811; 95% CI 0.666-0.987; p = 0.037) and longest fixation period (OR 0.798; 95% CI 0.691-0.921; p = 0.002), were the eye-movement parameters that were independently associated with diagnosis, after adjustment for sex (OR 4.35; 95% CI 1.516-12.483; p = 0.006) and visuospatial/executive score in Montreal Cognitive Assessment (OR 0.422; 95% CI 0.233-0.764; p = 0.004). The area under the ROC curve was determined to 0.817; 95% (CI) 0.732-0.901. CONCLUSION: Eye-tracking based measurements of gaze fixation and pupil reaction may be useful biomarkers of PD diagnosis. However, larger studies of eye-tracking parameters integrated into the screening of patients with suspected PD are necessary, to further investigate and confirm their diagnostic value.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".