Examining the validity of eye tracking during the computerized Wisconsin card sorting test in a sample of stroke patients and healthy controls
Bibliographic record
Abstract
INTRODUCTION: Each year, approximately 50,000 Canadians, one million Americans, and millions of people worldwide are hospitalized for stroke. Cognitive impairment is common after experiencing a stroke and is known to affect functioning on daily tasks. While neuropsychological assessments are often employed to assess cognitive abilities and make inferences about functional capabilities, there is growing interest in integrating contemporary technologies to augment assessment. Eye tracking allows previously overlooked information, such as overt visual attention based on fixations and saccades, to be quantified to help elucidate how responses are made during testing. METHOD: = 46). RESULTS: Results provided supporting evidence for the construction, criterion, and ecological validity of eye tracking on the cWCST with inpatients recovering from a stroke. Specifically, eye tracking metrics differentiated between inpatients and controls; fixations on cWCST areas of interest differed between type of response (conceptual versus non-conceptual); and average time per fixation predicted functional status early after a stroke as well as recovery during inpatient rehabilitation, above-and-beyond cWCST scores. Time spent on testing negated the effects of fixation and saccade counts for predicting cWCST performance, due to the substantial overlap in variance. CONCLUSION: Current findings of this preliminary study provided support for the validity of eye tracking, integrated with the cWCST, for inpatients recovering from a stroke. Implications and areas for future research are discussed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".