Markers of too little effort or too much alertness during neuropsychological assessment: Demonstration with perioperative changes
Bibliographic record
Abstract
OBJECTIVE: Cognitive assessment is based on performance in different tests. However, this performance might be hindered by lack of effective effort on the one hand, and by too much stress on the other hand. Despite their known impact, there are currently no effective tools for measuring cognitive effort or stress effect during cognitive assessment. We developed real-time electrophysiological markers for cognitive effort and for stress effect, which could be used during cognitive assessment. METHODS: We assessed these markers during the use of the Montreal Cognitive Assessment (MoCA) before and after cardiac surgery, which is known to involve cognitive decline in up to 30%-50% of elderly patients. RESULTS: The major findings of the study, for the largest group of patients, with preoperative MoCA in the intermediate range, were that the decline is significantly associated (1) with higher preoperative cognitive effort and (2) with higher postoperative stress effect during the test. CONCLUSIONS: These findings, as well as preliminary additional ones, suggest a potential importance for monitoring cognitive effort and stress effect during assessment in general, and specifically during perioperative assessment. SIGNIFICANCE: Easy-to-use markers could improve the efficacy of cognitive assessment and direct treatment generally, and specifically for perioperative decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".