Using eye-tracking technology to measure cognitive function in mild traumatic brain injury: A scoping review
Bibliographic record
Abstract
Cognitive impairment is a common symptom of mild traumatic brain injury (mTBI) and can have long term cognitive and behavioral consequences. Despite this, there is no universally accepted protocol for assessment of cognition in this population. Conventional neuropsychological assessment tools rely on verbal or manual responses which lend themselves to confounding factors such as stress, intelligence, initiation, and motivation, suggesting the need for more objective tools. A scoping review was undertaken to explore the utility of eye-tracking methods for detecting cognitive impairment in mTBI patients, and to survey the kinds of tasks used in this context. Six academic databases were searched for studies related to brain injury, eye tracking, and cognition. Data from 17 articles were extracted and synthesized. In most cases, neuropsychological and eye-tracking methods were in accordance when detecting cognitive impairment. However, in many cases, eye-tracking measures detected impairments when neuropsychological tasks did not. This review suggests that eye tracking could provide an effective, objective method to measure cognitive impairment in mTBI.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".