Evaluation of the SCAT 5 tool in the assessment of concussion in Para athletes: a Delphi study
Bibliographic record
Abstract
OBJECTIVES: To investigate if the sport concussion assessment tool version 5 (SCAT5) could be suitable for application to Para athletes with a visual impairment, a spinal cord injury, or a limb deficiency. METHODS: A 16-member expert panel performed a Delphi technique protocol. The first round encompassed an open-ended questionnaire, with round 2 onwards being composed of a series of closed-ended statements requiring each expert's opinion using a five-point Likert scale. A predetermined threshold of 66% was used to decide whether agreement had been reached by the panel. RESULTS: The Delphi study resulted in a four-round process. After round 1, 92 initial statements were constructed with 91 statements obtaining the targeted level of agreement by round 4. The expert panellist completion rate of the full four-round process was 94%. In the case of athletes with a suspected concussion with either limb deficiencies or spinal cord injuries, the panel agreed that a baseline assessment would be needed on record is ideal before a modified SCAT5 assessment. With respect to visual impairments, it was conceded that some tests were either difficult, infeasible or should be omitted entirely depending on the type of visual impairment. CONCLUSION: It is proposed that the SCAT5 could be conducted on athletes with limb deficiencies or spinal cord injuries with some minor modifications and by establishing a baseline assessment to form a comparison. However, it cannot be recommended for athletes with visual impairment in its current form. Further research is needed to determine how potential concussions could be more effectively evaluated in athletes with different impairments.
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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 | 0.120 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".