Validation of a telemedicine tool for patient monitoring in clinical dementia trials
Bibliographic record
Abstract
Abstract Background With the current pandemic crisis, now even more than ever, remote solutions such as telephone or telemedicine platforms are of great importance to provide isolated elderly people with timely access to health care. This study aims to determine feasibility and reliability of an automated tool utilizing artificial intelligence (AI) to facilitate large‐scale population‐based neurocognitive pre‐screening and monitoring of potential clinical trial participants. Method 21 participants (out of 120 still to include) over age 55 with and without cognitive impairment were administered in two conditions, once by telemedicine and once by face‐to‐face, a neuropsychological assessment consisting of 8 neurocognitive tests. The administration procedure was randomized. Each participant was asked to complete an acceptability questionnaire regarding the experience of being evaluated through a telemedicine tool. Results Similar results were obtained on the several cognitive test measures when comparing the remote to the face‐to‐face administration method. Word recall (r=0,928) and picture naming task (r=0,939) showed the strongest correlation. Acceptability of the tool was relatively high with preference even in the remote method for more convenience. Conclusion Results support the feasibility and reliability of remote cognitive testing through administration via a telemedicine tool. These systems can be used for remote disease monitoring, enabling patients to be assessed in their own homes and improve utilization of expert assessors allowing them to conduct neurocognitive testing remotely.
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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 | 0.114 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".