Precision of the Integrated Cognitive Assessment for the assessment of neurocognitive performance in athletes
Bibliographic record
Abstract
Abstract Choice reaction time tests are commonly used for the assessment of cognitive function, and may be useful to assess the effect of sport participation. This study investigated the precision of the Integrated Cognitive Assessment (ICA; Cognetivity Neurosciences Ltd., Vancouver, Canada) test for the assessment of cognitive function in athletes. Thirty-one participants volunteered to take part in this study, from both contact ( n = 22) and non-contact sports ( n = 9). Participants performed the ICA test consecutively both before and after normal training session to simulate resting and post-sport conditions. Precision errors, relationships (Pearson’s r), and internal consistency (Cronbach’s Alpha) were calculated for three variables, ICA Index (overall information processing ability), ICA Speed (information processing speed) and ICA Accuracy (information processing accuracy). ICA precision errors [root mean squared-standard deviation, RMS-SD (coefficient of variation, %CV)] pre-sport were ICA Index: 5.18 (7.14%), ICA Speed: 3.98 (4.64%), and ICA Accuracy: 3.64 (5.00%); and post-sport were ICA Index: 3.96 (4.94%), ICA Speed: 2.14 (2.32%), and ICA Accuracy 3.40 (4.25%). The ICA test demonstrates high in-vivo precision with all variables except ICA Index (7.14%) demonstrating an acceptable precision error of ≤5% %CV. All variables demonstrated strong relationships between consecutive tests pre- and post-sport (r ≥ 0.8) except for the ICA Index post-sport which demonstrated a moderate (r ≥ 0.5) relationship. The ICA Index demonstrated good internal consistency (α ≥ 0.8) for both pre-and post-sport. The ICA Speed and ICA Accuracy variables demonstrated excellent internal consistency (α ≥ 0.9) for both pre-and post-sport. The ICA test is suitable for the assessment of cognitive function pre- and post-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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".