3.11 Do sex and sport type matter? symptom severity, quality of life, vestibulo-ocular, oculomotor, and cervical spine findings following concussion
Bibliographic record
Abstract
Objective To evaluate the association between symptom severity score (SSS), quality of life (QOL), vestibulo-ocular reflex (VOR), oculomotor, and cervical spine outcomes and 1) sex and 2) sport type following mild Traumatic Brain Injury (mTBI). Design Cross-sectional. Setting Acute sport concussion clinics. Participants Youth aged 6–17 years diagnosed with concussion. Independent Variables Sex (male/female), sport type at time of injury (contact, non-contact, other, unknown) (Rice, 2008) Outcome Measures SSS (22 symptoms rated 0–6,/132), QOL [Pediatric Quality of Life inventory (PedsQL), VOR [mean gain on ICS video Head Impulse], oculomotor [King Devick (KD), total time (seconds)], cervical spine [Cervical Flexion Rotation Test (positive/negative), Cervical range of motion (full/limited)]. Main Results Forty-six males and 55 females reported contact (n=62), non-contact (n=18) and other (n=18) sports. Youth injured in contact sport had significantly worse VOR gain to the left [VORcontact= 0.91 (IQR= 0.85–0.96), VORnon-contact= 1.00 (IQR= 0.94–1.09), p= 0.046] and cervical ROM (proportion limitedcontact= 6.45%, proportion limitednon-contact= 0%, p <0.001), but better King Devick scores [KDcontact= 54.30s (IQR= 47.15–64.19), KDnon-contact= 69.65s (IQR= 51.37–77.00), p= 0.038) than other sport types. No significant differences were found in other outcomes by sport type or for any outcomes by sex. Conclusions Youth injured in contact sport were more likely to have limited cervical ROM, worse VOR gain to the left, and better KD scores than other sport types following concussion. There were no differences in any outcomes by sex. Further research to understand the mechanism driving the differences between sport types is warranted.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".