Infographic. The first position statement of the Concussion in Para Sport Group
Bibliographic record
Abstract
BackgroundA concussion is a common injury in many sports, including para sport. Aside from a more comprehensive need for concussion education, clinicians face difficulties applying concussion assessment and management guidelines to para athletes.1 At present, there is a lack of para-sport concussion research, and prior International Concussion in Sport (CIS) consensus papers have not addressed this specific population. To rectify this issue and improve concussion management provided to para athletes, the Concussion in Para Sport (CIPS) multidisciplinary expert group was created.2MethodsThe CIPS group undertook an in-depth analysis of issues specific to the para athlete within the established key clinical domains of the current (2017) Consensus Statement on Concussion in Sport.3 The existing Sports Concussion Assessment Tool 5 (SCAT5) was evaluated as part of this process and helped identify para athlete-specific concerns. Four CIPS working groups were tasked with exploring the following key clinical areas of concussion in para sport described in the most recent consensus statement of concussion in sport2:
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.491 | 0.236 |
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".