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One-year test-retest reliability of ten vision tests in Canadian athletes

2019· preprint· en· W4238120755 on OpenAlexafffundabout
Mehdi Aloosh, Suzanne Leclerc, Stephanie Long, Guowei Zhong, James M. Brophy, Tibor Schuster, Russell Steele, Ian Shrier

Bibliographic record

VenueF1000Research · 2019
Typepreprint
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsJewish General HospitalMcMaster UniversityMcGill UniversityImpact
FundersMitacs
KeywordsIntraclass correlationConcussionMedicineVergence (optics)AthletesReliability (semiconductor)Physical therapyPoison controlInjury preventionClinical psychologyArtificial intelligencePsychometrics

Abstract

fetched live from OpenAlex

<ns4:p> <ns4:bold>Background</ns4:bold> : Vision tests are increasingly being suggested for use 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. Therefore, our objective was to determine the one-year test-retest reliability of these tests. </ns4:p> <ns4:p> <ns4:bold>Methods</ns4:bold> : 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, etc.). Data were collected from clinical charts. We evaluated test-retest reliability using Intraclass Correlation Coefficient (ICC) and 95% limits of agreement (LoA). </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> 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 0.55 when an outlier was excluded. There was 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 Saccade (ICC=0.56). The ICC for Positive Fusional Vergence at 3m (ICC=0.56) also decreased to 0.45 after removing one outlier. 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%. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Four tests had 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. </ns4:p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.082
GPT teacher head0.401
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2019
Admission routes3
Has abstractyes

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