Prevalence of Convergence Insufficiency-Type Symptomatology in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Individuals with Parkinson's disease (PD) often present with visual symptoms (e.g., difficulty in reading, double vision) that can also be found in convergence insufficiency (CI). Our objective was to estimate the prevalence of CI-type visual symptomatology in individuals with PD, in comparison with controls. METHODS: Participants ≥50 years with (n=300) and without (n=300) PD were recruited. They were administered the Convergence Insufficiency Symptom Survey (CISS-15) over the phone. A score of ≥21 on the CISS-15, considered positive for CI-type symptomatology, served as the cutoff. Data from individuals (n=87 with, n=94 without PD) who were approached but who reported having a known oculovisual condition were analysed separately. Student's t test and chi-square at the 0.05 level were employed for statistical significance. RESULTS: A total of 29.3% of participants with versus 7.3% without PD presented with a score of ≥21 on the CISS-15 (p=0.001). Of the participants having a known oculovisual condition, 39.1% with versus 19.1% without PD presented with a score of ≥21 on the CISS-15 (p=0.01). CONCLUSIONS: The prevalence of CI-type visual symptoms is higher in individuals with versus without PD whether or not they have a coexisting oculovisual condition. These results suggest that PD per se places individuals with the disease at greater risk of visual symptomatology. These results further underline the importance of providing regular eye exams for individuals 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".