Screening by Telephone in the Alzheimer's Disease Anti-inflammatory Prevention Trial
Bibliographic record
Abstract
Compared with in-person assessment methods, telephone screening for dementia and other cognitive syndromes may improve efficiency of large population studies or prevention trials. We used data from the Alzheimer's Disease Anti-Inflammatory Prevention Trial to compare performance of a four-test Telephone Assessment Battery (TAB) that included the Telephone Interview for Cognitive Status (TICS) to that of a traditional in-person Cognitive Assessment Battery. Among 1,548 elderly participants with valid telephone and in-person screening results obtained within 90 days of each other, 225 persons were referred for a full cognitive diagnostic evaluation that was completed within six months of screening. Drawing on results from this panel of 225 individuals, we used the Capture-Recapture method to estimate population numbers of cognitively impaired participants. The latter estimates enabled us to compare the performance characteristics of the two screening batteries at specified cut-offs for detection of dementia and milder forms of impairment. Although our results provide relatively imprecise estimates of the performance characteristics of the two batteries, a comparison of their relative performance suggests that, at selected cut-off points, the TAB produces results broadly comparable to in-person screening and may be slightly more sensitive in detecting mild impairment. TAB performance characteristics also appeared slightly better than those of the TICS alone. Given its benefits in time and cost when screening for cognitive disorders, telephone screening should be considered for large samples.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".