Influence of the Intensive Care Unit Environment on the Reliability of the Montreal Cognitive Assessment
Bibliographic record
Abstract
Background: Neuropsychological screening becomes increasingly important for the evaluation of subarachnoid hemorrhage (SAH) and stroke patients. It is often performed during the surveillance period on the intensive (ICU) already, while it remains unknown, whether the distraction in this environment influences the results. We aimed to study the reliability of the Montreal Cognitive Assessment (MoCA) in the ICU environment. Methods: Consecutive stable acute brain injury patients were evaluated twice within 36 hours using official parallel versions of the MoCA (ΔMoCA). The sequence of assessment was randomized into a) busy ICU first or b) quiet office first with subsequent crossover. For repeated MoCA, we determined sequence, period, location effects, and the intraclass correlation coefficient (ICC). Results: N=50 patients were studied (n=30 (60%) male), with a mean age of 57 years (standard deviation (SD) 17.2). The assessment’s sequence (“ICU first” mean ΔMoCA -1.14 (SD 2.34) vs. “Office first” -0.73 (SD 1.52)) did not influence the MoCA (p=0.47). On the 2nd period, participants scored 0.96 points worse (SD 2.01; p=0.001), indicating no MoCA learning effect but a possible difference in parallel versions. There was no location effect (p=0.31) with ΔMoCA between locations (Office minus ICU) of -0.32 (SD 2.21). The ICC for repeated MoCA was 0.87 (95% CI 0.79 – 0.92). Conclusions: The reliability of the MoCA was excellent, independent from the testing environment being ICU or office. This finding is helpful for patient care and studies investigating the effect of therapeutic interventions on the neuropsychological outcome after SAH, stroke or traumatic brain injury.
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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 | 0.007 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".