Significance of the Boston Cognitive Assessment in patients with chronic post-stroke cognitive impairment
Bibliographic record
Abstract
Background The Boston Cognitive Assessment (BoCA) is an online, self-administered, remote cognitive screening tool for the early detection and long-term monitoring of health changes in the brain of aging populations. This study aimed to evaluate the reliability and validity of the Mandarin version of the BoCA in Chinese patients with stroke, thereby providing a reference for important clinical applications. Methods This study included 120 patients with chronic stroke and 120 healthy controls. All participants completed the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Mandarin versions of the BoCA. Assessments were spaced a minimum of 60 min apart. For the test–retest reliability analysis, 120 patients with stroke were retested on the BoCA after 1 week. The receiver operating characteristic (ROC) curves were generated to assess diagnostic performance. Results Among the stroke group, the BoCA showed appreciable internal consistency (Cronbach’s α = 0.808) and significant test–retest reliability (intraclass correlation coefficient, ICC = 0.895, p < 0.001). The BoCA subscales demonstrated moderate-to-strong correlations with the total score ( r = 0.546–0.770), supporting adequate content validity. The BoCA total scores were strongly correlated with the MMSE ( r = 0.829, p < 0.001) and MoCA ( r = 0.848, p < 0.001) scores, demonstrating adequate criterion-related validity. Exploratory factor analysis (EFA) of the BoCA tasks revealed one robust factor accounting for a plurality (i.e., 46.9%) of the total variance, indicating sufficient construct validity. An ROC analysis revealed comparable diagnostic performance for the BoCA (area under the curve, AUC = 0.823), MMSE (AUC = 0.836), and MoCA (AUC = 0.818). A BoCA score of 23.5 distinguished the stroke group from the control group with 81.7% sensitivity and 69.2% specificity. Conclusion The Mandarin version of the BoCA exhibits significant reliability and validity and functions effectively as a supplementary measure for cognitive assessment in stroke survivors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".