Prevalence of Convergence Insufficiency in Parkinson's Disease
Bibliographic record
Abstract
Abstract Background We recently reported that convergence insufficiency (CI)‐type visual symptomatology was more prevalent in participants with Parkinson's disease (PD), compared to controls. The objective of this work was to determine the prevalence of a confirmed clinical diagnosis of CI in PD, compared to controls. Methods Participants with (n = 80) and without (n = 80) PD were recruited and received an eye exam. Published criteria were used to arrive at a clinical diagnosis of CI. The Convergence Insufficiency Symptom Survey (CISS‐15) questionnaire was administered to each participant, with a score of ≥21 being considered positive for CI symptomatology. Student t test, chi‐square, or nonparametric tests at the 0.05 level were used for statistical significance. Results A total of 43.8% of participants with versus 16.3% without PD had a clinical diagnosis of CI (P ≤ 0.001). A total of 53.8% of participants with versus 18.8% without PD had scores on the CISS‐15 of ≥21 (P ≤ 0.001). Conclusions These results indicate that individuals with PD have a higher prevalence of CI and CI symptomatology than controls. These data provide evidence supporting the notion that treatment for symptomatic CI should be investigated in 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) | 0.002 | 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".