Development of the Brain Health Outcome Scale: A Polyomino-Based Integration of Functional and Cognitive Assessments After Stroke
Bibliographic record
Abstract
Introduction Stroke survivors often face significant functional and cognitive impairments impacting their quality of life. Existing outcome measures, such as the modified Rankin Scale (mRS), inadequately capture cognitive aspects of recovery. This study aims to develop a comprehensive Brain Health Outcome Scale (BHOS) integrating both functional and cognitive assessments to address this critical gap. Methods Clinical data were drawn from the prospective stroke registry of two hospitals, involving 1,678 patients assessed with mRS, the Korean version of Mini-Mental State Examination (K-MMSE), and the Korean version of Instrumental Activities of Daily Living (K- IADL) scores. BHOS was created using a structured polyomino-based approach. A 24-cell grid was constructed by combining six groups derived from mRS scores (0 to 5, totaling six groups) and four standardized cognitive groups from K-MMSE scores using Z-scores (normal [-1≤Z], mild [-2≤Z<-1], moderate [-3≤Z<-2], severe [Z<-3]). Specific proximity and connectivity rules ensured each group comprised two to six adjacent squares, including permitted diagonal arrangements. Polyomino theory identified 110 candidate shapes, including dominoes, trominos, tetrominos, pentominos, hexominos, and diagonal configurations. Fifty-nine shapes anchored at the top-left corner yielded 408,500 unique configurations after redundancy reamoval. Cluster analysis using Welch’s F-statistic optimized intra-cluster homogeneity and inter-cluster differences in K-IADL scores in each group, refining the BHOS groupings Results From 408,500 configurations, the top 20 with the highest Welch’s F-statistic scores were selected. Among these, optimal grouping systems demonstrated clear differentiation regarding K-IADL scores, with no overlaps in their respective 95% confidence intervals. Furthermore, the selected configurations showed a consistent sequential ordering of groups from the top-left to the bottom-right areas of the grid, enhancing interpretability and practical utility. Conclusions The developed BHOS successfully integrates cognitive and functional measures, offering clear differentiation and intuitive interpretation of post-stroke brain health outcomes. Future studies are planned to evaluate whether BHOS scores correlate more closely with long-term outcomes including mortality, institutionalization, incident dementia and medical costs, compared to traditional measures such as mRS and K-MMSE.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".