3.34 Objective, digital vestibular-ocular motor screening – an interim, exploratory analysis
Bibliographic record
Abstract
Objective We undertook an exploratory, interim analysis examining changes in ocular vergence using digital vestibular-ocular motor screening (VOMS) assessment from baseline to post-concussion. Design Prospective observation. Setting Professional and amateur sport teams. Participants 379 (96 female) Australian rules footballers, aged over 18 years, male and female competitions. Interventions (or Assessment of Risk Factors) Ocular vergence from the following VOMS assessment (NeuroFlex Inc., Canada); smooth pursuit eye tracking (head free and head fixed) vestibular ocular reflex (VOR) (horizontal and vertical) saccades and antisaccades optokinetic nystagmus Outcome Measures VOMS assessments occurred at baseline (pre-season) and following a concussion (with 48 hours). Changes examined with a paired-samples, one-tailed t-test, with significance at 0.05. Two analyses were performed (table 1); ocular vergence for all protocols three tests focusing on ‘central’ measures: smooth pursuit head-fixed, horizontal VOR, and saccades Main Results At the time of analysis, there were 14 concussions (6 females, age 22 [range 18–29]). Changes in mean ocular vergence for all tests trended towards significance (t13 = 1.6, p = 0.067). There was significantly increased divergence from baseline to post-concussion (t13 = 2.1, p = 0.029) for the central tests. Conclusions There was a trend towards a significant increase in mean ocular divergence after a concussion. For ‘central’ VOMS tests, ocular divergence significantly increased from baseline to post-concussion.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".