Pac-12 CARE-Affiliated Program: structure, methods and initial results
Bibliographic record
Abstract
Sport-related concussion has garnered increasing scientific attention and research over the last decade. Collegiate student-athletes represent an important cohort in this field. As such, the Pac-12 CARE-Affiliated Program (CAP) was formed in 2017 as a regional hub of the Concussion Assessment, Research and Education (CARE) consortium. CAP is multisite, prospective, longitudinal study that aims to improve student-athlete health by identifying factors associated with concussion incidence and recovery and using this knowledge to inform best clinical practices and policy decisions. CAP employed a staggered rollout across the Pac-12, with the first four institutions enrolling in fall 2018. After receiving institutional review board (IRB) approval, these institutions began consenting student-athletes to share clinical concussion and baseline data for research purposes. Athletes completed baseline testing that included a medical questionnaire, concussion history and a battery for clinical concussion assessments. Concussed student-athletes were given the same battery of assessments in addition to full injury and return to play reports. Clinicians at each university worked with a data coordinator to ensure appropriate reporting, and the Pac-12 Concussion Coordinating Unit at the University of Colorado Boulder provided oversight for quality control of the data study wide. During year 1, CAP consented 2181 student-athletes and tracked 140 concussions. All research was conducted with the appropriate IRB approval across the participating Pac-12 institutions. Data security and dissemination are managed by the Presagia Sports Athlete Electronic Health Record software (Montreal, Quebec, Canada) and QuesGen Systems (San Francisco, California, USA).
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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 | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".