Differences in Rehabilitation Needs after Stroke: A Similarity Analysis on the ICF Core Set for Stroke
Bibliographic record
Abstract
Background: Successful rehabilitation is associated with physical, psychological, environmental, social, and personal factors based on the International Classification of Functioning, Disability and Health (ICF) framework. The influence of age has been suggested as crucial personal factors that may affect rehabilitation needs in post-stroke survivors. The aim of this study was to investigate the qualifiers of the ICF core set for stroke to detect differences in rehabilitation needs and goals between older (O, >65 years old) and younger (Y, ≤65 years old,) post-stroke individuals. Materials and methods: In this observational study, the comprehensive core set for stroke was filled during the rehabilitation period. Patient information was obtained using disability scales and translated into certain ICF categories using linking rules. Frequency, similarity, and linear regression analyses were performed for ICF qualifier profiles among Y and O patients. Results: Forty-eight ICF variables were significantly different between Y (n = 35, 46.17 ± 11.27 years old) and O (n = 35, 76.43 ± 6.77 years old) patients. Frequency analysis showed that activity of daily living and basic needs were more prevalent in O patients, whereas regaining of social role and social life were more prevalent in Y patients. The average Jaccard Index result (similarity analysis) was more homogeneous in O than in Y patients. Conclusions: ICF qualifiers are useful to design patient-centered care. Y patients have more heterogeneous needs and require more personalized program than O patients.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".