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).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".