Point-of-Care Testing Using a Neuropsychology Pocketcard Set: A Preliminary Validation Study
Bibliographic record
Abstract
Neurocognitive screening instruments usually require printed sheets and additional accessories, and can be unsuitable for low-threshold use during ward rounds or emergency workup, especially in patients with motor impairments. Here, we test the utility of a newly developed neuropsychology pocketcard set for point-of-care testing. For aphasia and neglect assessment, modified versions of the Language Screening Test and the Bells Test were validated on 63 and 60 acute stroke unit patients, respectively, against expert clinical evaluation and the original pen-and-paper Bells Test. The pocketcard aphasia test achieved an excellent area under the curve (AUC) of 0.94 (95% CI: 0.88−1, p < 0.001). Using an optimal cut-off of ≥2 mistakes, sensitivity was 91% and specificity was 81%. The pocketcard Bells Task, measured against the clinical neglect diagnosis, achieved higher sensitivity (89%) and specificity (88%) than the original paper-based instrument (78% and 75%, respectively). Separately, executive function tests (modified versions of the Trail Making Test [TMT] A and B, custom Stroop color naming task, vigilance ‘A’ Montreal Cognitive Assessment item) were validated on 44 inpatients with epilepsy against the EpiTrack® test battery. Pocketcard TMT performance was significantly correlated with the original EpiTrack® versions (A: r = 0.64, p < 0.001; B: r = 0.75, p < 0.001). AUCs for the custom Stroop task, TMT A and TMT B for discriminating between normal and pathological EpiTrack® scores were acceptable, excellent and outstanding, respectively. Quick point-of-care testing using a pocketcard set is feasible and yields diagnostically valid information.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".