One-year test-retest reliability of ten vision tests in Canadian athletes
Bibliographic record
Abstract
Background : Vision tests are used in concussion management and baseline testing. Concussions, however, often occur months after baseline testing and reliability studies generally examine intervals limited to days or one week. Our objective was to determine the one-year test-retest reliability of these tests. Methods : We assessed one-year test-retest reliability of ten vision tests in elite Canadian athletes followed by the Institut National du Sport du Quebec. We included athletes who completed two baseline (preseason) annual evaluations by one clinician within 365±30 days. We excluded athletes with any concussion or vision training in between the annual evaluations or presented with any factor that is believed to affect the tests (e.g. migraines). Data were collected from clinical charts. We evaluated test-retest reliability using Intraclass Correlation Coefficient (ICC) and 95% limits of agreement (LoA). Results: We examined nine female and seven male athletes with a mean age of 22.7 (SD 4.5) years. Among the vision tests, we observed excellent test-retest reliability in Positive Fusional Vergence at 30cm (ICC=0.93) but this dropped to 0.53 when an outlier was excluded in a sensitivity analysis. There was good to moderate reliability in Negative Fusional Vergence at 30cm (ICC=0.78), Phoria at 30cm (ICC=0.68), Near Point of Convergence break (ICC=0.65) and Saccades (ICC=0.61). The ICC for Positive Fusional Vergence at 3m (ICC=0.56) also decreased to 0.45 after removing two outliers. We found poor reliability in Near Point of Convergence (ICC=0.47), Gross Stereoscopic Acuity (ICC=0.03) and Negative Fusional Vergence at 3m (ICC=0.0). ICC for Phoria at 3m was not appropriate because scores were identical in 14/16 athletes. 95% LoA of the majority of tests were ±40% to ±90%. Conclusions: Five tests had good to moderate one-year test-retest reliability. The remaining tests had poor reliability. The tests would therefore be useful only if concussion has a moderate-large effect on scores.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".