Telephonic Remote Evaluation of Neuropsychological Deficits (TREND): Longitudinal Monitoring of Elderly Community-dwelling Volunteers Using Touch-tone Telephones
Bibliographic record
Abstract
Use of interactive voice response (IVR) technology to monitor cognitive functioning in cognitively normal (CN), mild cognitive impairment (MCI), and mild dementia (MD) participants was examined using 107 community-dwelling participants, 65 to 88 years old. Baseline Clinical Dementia Ratings identified 36 participants as CN, 37 with MCI, and 34 as MD. Alzheimer's Disease Assessment Scale (ADAS) and Mini-Mental State Examinations were administered during clinic visits at weeks 0, 8, 16, and 24. IVR cognitive testing was completed at each visit and from participants' homes at weeks 4, 12, and 20. Study partners provided dementia symptoms severity ratings via IVR. The assessment system received 719 participant and 723 partner calls. All calls initiated by CN participants, 99.2% by MCI participants, and 87.3% by MD participants were completed. Telephonic Remote Evaluation of Neuropsychological Deficit tasks showed significant performance differences between participant groups, good reliability, and convergent validity with Mini-Mental State Examinations and ADAS-Cog measures. Automated cognitive testing calls took about 18 minutes to complete, and informant calls took approximately 4 minutes. IVR informant data were convergent with the ADAS-Noncog measure. Computer-automated assessments of cognitive functioning via IVR provided reliable, valid data. Such assessments might benefit routine clinical care and large-scale, longitudinal research in the future, but will require additional research over longer periods.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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 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".