Telephone-based cognitive screening for stroke patients in china
Bibliographic record
Abstract
BACKGROUND: Valid telephone assessment for cognitive impairment is lacking in stroke settings. We investigated the feasibility and validity of the 5-minute National Institute of Neurological Disorders and Stroke and Canadian Stroke Network (NINDS-CSN) protocol and six-item screener (SIS) in stroke patients by telephone administration. METHODS: Patients were assessed with a comprehensive face-to-face neuropsychological assessment after three months of stroke onset, followed by the 5-minute NINDS-CSN protocol (30 points) and SIS (6 points) at least one month later. Administration time was recorded for the telephone tests. Validity of both tests was determined using the area under the receiver operating characteristics curve (AUC). RESULTS: Eighty-nine patients (age, 62.9 ± 8.6 years; male, 65.2%) received a face-to-face assessment and 80 completed telephone tests. The time required to administer the 5-minute NINDS-CSN protocol was 4.3 ± 1.0 minutes, and SIS 57.3 ± 17.7 seconds. Validity of detecting cognitive impairment as assessed by AUC was 0.86 (95% CI, 0.78-0.94) for 5-minute NINDS-CSN protocol, and 0.74 (95% CI, 0.63-0.85) for SIS. Sensitivity and specificity were optimal with the cut-off values of 23.5/24 for the 5-minute NINDS-CSN protocol, and 4/5 for SIS. CONCLUSIONS: Both the telephone-based 5-minute NINDS-CSN protocol and SIS were feasible and valid in screening cognitive impairment after stroke in China.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".