Detecting persistent attention impairments in high functioning young adults with a mild closed head injury
Bibliographic record
Abstract
Mild closed head injuries (mCHI) are commonly associated with reports of persistent attention and memory impairment yet standardized neuropsychological tests often prove unable to detect these complaints. The purpose of this study was to determine if a more sensitive measure of attention impairment would better dissociate mCHI participants from controls. The Slip Induction Task was administered to 43 undergraduate students (21 mCHI). This task involves participants learning a sequence of hand movements to targets and after the sequence is well learned, occasionally requiring unexpected movements. These changes to the routine often induced action slips, where the participant incorrectly moved to the expected target. Surprisingly, we found that mCHI participants made fewer action slips than controls. However, when the unexpected movement was completed correctly, the mCHI participants took significantly longer to make this change than controls. Furthermore, only mCHI participants' speed on altered sequences predicted their likelihood of making errors. These results suggest that while the mCHI participants were more accurate, this came at a cost with respect to the amount of time required to correctly complete an unexpected movement. This implies that changes to the routine placed greater demands on the mCHI participants as reflected in a reduction in speed to compensate for their comparable accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".