Sensitivity of the Cogstate Test Battery for Detecting Prolonged Cognitive Alterations Stemming From Sport-Related Concussions
Bibliographic record
Abstract
OBJECTIVE: To determine if the Cogstate test battery contains the requisite sensitivity to detect prolonged cognitive alterations. METHODS: One hundred twenty collegiate athletes (71 with a history of concussion; 49 controls) completed the Cogstate test battery, to which we added a 2-back condition. In addition to the Cogstate clinical (transformed variables), we analyzed the raw data. RESULTS: The clinical variables failed to reveal any group differences. Further, although the raw data failed to reveal group differences for tasks measuring lower-level cognition, group differences were observed for accuracy on the 1- and 2-back tasks, which require multiple aspects of higher cognition. The overall classification accuracy was higher using the raw data than the clinical variables. The combined sensitivity of the 1- and 2-back task was moderate and specificity was high. CONCLUSIONS: These results suggest that using the raw scores over clinical variables increases the sensitivity of the test battery. Moreover, these results add another piece of evidence suggesting that concussive injuries are associated with subtle long-term alterations in aspects of higher cognition. Importantly, these deficits would have gone unobserved if we had relied solely on automated clinical variables. The current results further our scientific understanding of concussion and may be used to advance clinical practices.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".