Is it really the result of a concussion? Lessons from a case study
Bibliographic record
Abstract
BACKGROUND: Within the last two decades, attitudes have shifted from considering sports-related concussion as an insignificant minor injury with no long-term repercussions to a potentially serious brain injury garnering attention from media, clinicians, researchers, and the general public. OBJECTIVES: To conduct a case study to determine the underlying cause of persistent issues suspected to be associated with a history of sports-related concussion. PROTOCOL: Participant A underwent neurophysiological testing following the Neary protocol (assessment of cerebrovascular and cardiovascular variables), comprehensive concussion assessment at a dedicated sports concussion clinic (history, neurological assessment, cervical spine screening, vestibulo-ocular screening, SCAT-5, and exercise testing), referral to a neurologist, structural MRI scan, and referral for specialised assessment at a dedicated dizziness and balance centre. RESULTS: Despite a history of multiple sports-related concussions, Participant A's persistent symptom reports were associated with peripheral vestibular dysfunction and otolithic dysfunction seemingly unrelated to his concussion history. DISCUSSION: Lessons from Participant A's case study showed that on-going symptoms that patients may associate with the effects of concussions may instead be due to unrelated causes that share similar symptomology. CONCLUSION: This research exemplifies the importance of a multi-disciplinary assessment using a repeated testing protocol.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".