Administration of Neuropsychological Tests Using Interactive Voice Response Technology in the Elderly: Validation and Limitations
Bibliographic record
Abstract
Interactive voice response (IVR) systems are computer programs, which interact with people to provide a number of services from business to health care. We examined the ability of an IVR system to administer and score a verbal fluency task (fruits) and the digit span forward and backward in 158 community dwelling people aged between 65 and 92 years of age (full scale IQ of 68-134). Only six participants could not complete all tasks mostly due to early technical problems in the study. Participants were also administered the Wechsler Intelligence Scale fourth edition (WAIS-IV) and Wechsler Memory Scale fourth edition subtests. The IVR system correctly recognized 90% of the fruits in the verbal fluency task and 93-95% of the number sequences in the digit span. The IVR system typically underestimated the performance of participants because of voice recognition errors. In the digit span, these errors led to the erroneous discontinuation of the test: however the correlation between IVR scoring and clinical scoring was still high (93-95%). The correlation between the IVR verbal fluency and the WAIS-IV Similarities subtest was 0.31. The correlation between the IVR digit span forward and backward and the in-person administration was 0.46. We discuss how valid and useful IVR systems are for neuropsychological testing in the elderly.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".