The Cognistat (neurobehavioural cognitive status exam): Administering the full test in stroke patients for optimal results
Bibliographic record
Abstract
BACKGROUND: One of the most commonly administered tools occupational therapists use for stroke patients is the Cognistat, which was designed as a brief screening tool of cognitive functioning. Evaluations in samples of patients have identified a high false-negative rate if the Cognistat is administered using the 'screen metric' approach. Assessing the Cognistat based on its intended design can ensure consistency and accuracy among occupational therapists for this commonly administered tool. Thus, this study examined the accuracy of administering the entire Cognistat in comparison to the screen-metric approach and the factor analytic structure within stroke patients. METHODS: The full Cognistat was administered to stroke patients receiving inpatient rehabilitation. RESULTS: Seventy-five patients who experienced a recent stroke met inclusion criteria. An inconsistency between the screen and metric items was found for five of 10 subscales. Additionally, a principal component analysis (PCA) found the Cognistat to be a two factor structure with six of the subscales loading on Factor 1, while the remaining subscales loaded on Factor 2. CONCLUSIONS: Our findings confirm that occupational therapists should administer the full Cognistat to stroke patients rather than the original screen-metric approach. A two-factor structure was also supported in our results, suggesting that occupational therapists' scoring practices should reflect this finding and use the differentiated score out of 10 rather than a global sum. However, additional research is necessary to consider the clinical and theoretical significance of the Cognistats' subscale clustering.
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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 | 0.001 | 0.002 |
| 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".