A computerized, self‐administered test of verbal episodic memory in elderly patients with mild cognitive impairment and healthy participants: A randomized, crossover, validation study
Bibliographic record
Abstract
Abstract Introduction Performance of “Revere”, a novel iPad‐administered word‐list recall (WLR) test, in quantifying deficits in verbal episodic memory, was evaluated versus examiner‐administered Rey Auditory Verbal Learning Test (RAVLT) in patients with mild cognitive impairment and cognitively normal participants. Methods Elderly patients with clinically diagnosed mild cognitive impairment (Montreal Cognitive Assessment score 24–27) and cognitively normal (Montreal Cognitive Assessment score ≥28) were administered RAVLT or Revere in a randomized crossover design. Results A total of 153/161 participants (Revere/RAVLT n = 75; RAVLT/Revere n = 78) were randomized; 148 (97%) completed study; 121 patients (mean [standard deviation] age: 70.4 [7.84] years) were included for analysis. Word‐list recall scores (8 trials) were comparable between Revere and RAVLT (Pearson's correlation coefficients: 0.12–0.70; least square mean difference [Revere‐RAVLT]: −0.84 [90% CI, −1.15; −0.54]). Model factor estimates indicated trial (P < .001), period (P < .001) and evaluation sequence (P = .038) as significant factors. Learning over trials index and serial position effects were comparable. Discussion Participants' verbal recall performance on Revere and RAVLT were equivalent.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".