Could everyday technology improve access to assessments? A pilot study on the feasibility of screening cognition in people with Parkinson's disease using the Montreal Cognitive Assessment via Internet videoconferencing
Bibliographic record
Abstract
BACKGROUND: The distances and distribution of people, and pressures on the health system in Australia mean that access to services for people living with a neurodegenerative condition may be inadequate. Telehealth may offer ways to provide timely and efficient monitoring and support. People with Parkinson's disease require regular screening of their symptoms and needs, but may have limited access to health professionals. Cognitive changes can impact on occupational performance, thus timely monitoring of cognition is important for informing occupational therapy interventions. AIM: To evaluate the feasibility of screening cognition in people with Parkinson's disease using available technology in their homes. METHOD: Eleven participants with Parkinson's disease completed the Montreal Cognitive Assessment face-to-face and then via videoconferencing one week later using the technology available at their home. Participants and assessors provided feedback on their experience. RESULTS: All Montreal Cognitive Assessment items could be completed over videoconference (e.g. Skype), with a median difference of 2 (IQR: 1-2.5) between face-to-face and videoconference scores. Higher scores were not favoured by either mode of assessment. Three participants received inconsistent cognitive classifications between the two assessment methods. Participant and assessor feedback indicated reported benefits including convenience as well as technological limitations. CONCLUSIONS: Given the pressures on the health system and the apparent acceptability to consumers, occupational therapists may explore the utility of readily accessible technology to enable timely monitoring of cognition for people with Parkinson's disease. Further research is needed to develop and demonstrate the reliability and validity of this approach.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".