Data from an emerging UK sports concussion clinic; should athlete assessment be sports-specific?
Bibliographic record
Abstract
Objective To provide an overview of the Cambridge Sports Concussion Clinic; to investigate trends in patients’ neurocognition utilising assessment with the Immediate Post-concussion Assessment and Cognitive Test (ImPACT) as stratified by sport. Background Professional sporting litigation in the USA has brought to the fore the issue of sports concussion. Despite this, UK outpatient management remains in its infancy, with less than five centres offering specialised post-injury review. This poster presents data from the Cambridge Sports Concussion Clinic (CSCC), comparing ImPACT assessments across motorsport and rugby. Design/Methods The data is a retrospective analysis of a prospectively maintained database cohort, in which demographic, clinical and neurocognitive data are archived. The submission includes CSCC patients who attended Addenbrooke’s Hospital, Cambridge for clinical review June 2017-March 2019, who were diagnosed with concussion and completed an ImPACT assessment. Results 36 post-injury reviews were completed across 19 clinical encounters with patients aged 15yrs+. 47% of athletes were injured during motorsport (saloon car, rally, motocross and single seater racing), 47% in rugby and 5% during equestrian activity. The majority were professional (47%) or competed in collegiate (21%) or high school (21%) competition with 1–13 years at their current sporting level (mean4.2 +/−SD 4.2 years). Only 22% of 18 patients were concussion-naïve prior to their current injury, with a range of 0–4 physician-confirmed diagnoses (1.7 +/− 1.3). Comparison of motorsport drivers versus rugby athletes reveals trends towards lower scores in ImPACT memory composite values (verbal F 0.57, t −1.4, p 0.15; memory F 2.1, t −0.9, p 0.37) and improved reaction time (F 3.3, t 1.8, p 0.08) with significant differences in visual motor speed (F 0.90, t−4.1, p < 0.001). Conclusions Preliminary cross-sport analyses indicate that motorsport competitors have worse composite memory scores, better reaction times and significantly altered visual motor speed. These early data provide support for sports-specific approaches.
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 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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".