The reliability of the Montreal Cognitive Assessment using telehealth in a rural setting with veterans
Bibliographic record
Abstract
BACKGROUND: Telehealth neuropsychological services can increase the availability of specialised care for individuals in rural areas where barriers to these services are faced. As this practice becomes more commonplace, the reliability and validity of neuropsychological assessment administered by telehealth continues to be established. The Montreal Cognitive Assessment, a screener for general neurocognitive dysfunction, may be particularly useful since this measure can be given by telehealth with minimal adaptation. METHODS: Veterans from a rural area of the country who were referred to an outpatient neuropsychology clinic were administered the Montreal Cognitive Assessment either in-person or by telehealth by a clinician. A second clinician observed the administration in-person or by telehealth and independently scored the each participant's performance. The inter-rater reliabilities across conditions were compared to assess for differences between in-person and telehealth consultations. RESULTS: The inter-rater reliability of the Montreal Cognitive Assessment across the three conditions of interest was acceptably high and values ranged from r = 0.88 to r = 0.98. Reliability correlations were compared and no significant differences among the conditions were observed ( p's > 0.10). Beyond reliability, univariate comparison of the absolute mean differences of clinician scores showed no significant differences among the actual raw scores of the three conditions tested, indicating good accuracy ( p = 0.56). CONCLUSIONS: The inter-rater reliabilities of Montreal Cognitive Assessment scores across conditions were all acceptably high, and administration of the Montreal Cognitive Assessment using telehealth technology did not significantly alter the total scores. Overall, the lack of significant differences suggests that administering the Montreal Cognitive Assessment by telehealth is reliable, accurate and well received by participants.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".