The Rapid Online Cognitive Assessment
Bibliographic record
Abstract
Abstract INTRODUCTION Paper-based screening examinations are well-validated but minimally scalable. If a DCA replicate paper-based screening, it would improve scalability while benefiting from their extensive validation. METHODS We developed and evaluated the Rapid Online Cognitive Assessment (RoCA) against gold-standard paper-based tests in patients with a range of cognitive integrity (n = 46). Patient perception of the RoCA was also evaluated with post-examination survey. RESULTS The RoCA classifies patients similarly to gold standard paper-based tests, with a receiver operating characteristic area under the curve of 0.81 (95%CI 0.67-0.91, p < 0.001). It achieves a sensitivity of 0.94 (95%CI 0.80-1.0, p < 0.001). This was robust to multiple control analyses. 83% of patient respondents reported the RoCA as highly intuitive, with 95% perceiving it as adding value to their care. DISCUSSION The RoCA may act as a simple and highly scalable cognitive screen.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".