Incidence of Remote Near-Point of Convergence in University Athletes After Sport-Related Concussion
Bibliographic record
Abstract
OBJECTIVE: Near-point of convergence (NPC) testing is an attractive screening tool in the sport setting because it is rapid, requires few resources, and is easy to administer. Remote NPC has been reported after sport-related concussion (SRC), although the incidence among a university-aged population is not well defined. The purpose of the study was to examine the incidence of remote NPC after SRC in a cohort of Canadian interuniversity athletes. DESIGN: Cross-sequential. SETTING: University. PARTICIPANTS: One hundred thirty-two university athletes [SRC, n = 68; musculoskeletal (MSK) injury, n = 64] were tested before the beginning of their competitive season and again after their injury. INDEPENDENT VARIABLES: Healthy athletes measured preseason were compared with athletes after SRC or MSK injury using both longitudinal and cross-sectional designs. MAIN OUTCOME MEASURES: Remote NPC (pass/fail), measured at 6 cm or greater, repeated 3 times. RESULTS: After SRC, 22% of athletes failed their test postinjury (95% CI, 14%-33%). Comparatively, in the MSK group, 3% of athletes failed their test postinjury (95% CI, 1%-7%). A direct comparison of both injury groups yielded a mean 19% higher prevalence of failed NPC tests after SRC versus MSK injury (95% CI, 10%-30%). There seems to be no relationship between reported symptom burden and NPC performance after SRC. CONCLUSION: Remote NPC occurs in approximately 1 of 5 athletes after SRC and is rarely observed after MSK injury.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".