The development of a threshold curve for the understanding of concussion in sport
Bibliographic record
Abstract
Much of what is known concerning human brain injury thresholds is based upon impacts to cadavers and animal models that were used to generate the Wayne State Concussion Tolerance Curve (WSTC) and similar curves. These curves are the foundation for predictive metrics used in standard development as well as helmet design. These curves were based upon a very narrow range of impacts; impacts whose characteristics differ greatly from how the head is impacted in sport. This research examines the uses of time-based curves like the WSTC in the context of understanding mechanisms of brain injury and head protection. Published linear/rotational acceleration magnitude/duration data from Hybrid III laboratory reconstructions of brain injury events were plotted. This research further develops the understanding of injury thresholds in comparison to threshold curves such as the WSTC and Brain Injury Curve Leuven. The data demonstrate the relationships between magnitude and duration of dynamic response on minor traumatic brain injury (mTBI) in sport.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".