Developing a data repository of standard concussion assessment clinical data for research involving college athletes
Bibliographic record
Abstract
In sports concussion research, obtaining quality data from a sufficient number of participants to reach statistical power has been a particular problem. In addition, the necessary requirements of accessibility, informed consent, and confidentiality must be met. There is need to develop more efficient and controlled methods for collecting data to answer research questions in this realm, but the ability to collect and store these data in an efficient manner at the local level is limited. By virtue of their training, neuropsychologists can play a key role in improving data collection quality. The purpose of this paper is to describe a data repository that has been developed in the context of a university sports medicine concussion management program that includes baseline and postinjury data from student athletes. Diagnostic information, basic health information, current symptoms, neuropsychological test data, balance and vestibular data, and visual processing data are currently included in the standard of care for athletes; however, the process described need not be limited to these types of data. While a national traumatic brain injury (TBI) data repository has been developed by the National Institute of Health (NIH), local repositories have not yet become common. Thus, the description of this project is of value at the local level in the United States and internationally.
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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 | 0.113 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".