Bottom-up versus Top-down designed rehabilitation sessions in chronic stroke survivors: a pilot randomized controlled trial
Bibliographic record
Abstract
Purpose The present study aimed to compare the effectiveness of Top-down and Bottom-up approaches on levels of the International Classification of Functioning, Disability and Health Framework (ICF), including impairments, activities, and participation.Materials and methods Thirty-nine chronic stroke survivors were recruited for this single-blinded randomized clinical trial. Participants were assigned to Top-down, Bottom-up interventions, or control group, and received a 6-week intervention. They were assessed before/after treatments and at follow-up (6 weeks later). Impairments were measured through kinematic analysis, Trail Making Tests (TMT), and Fugl-Meyer Assessment (FMA). Activity and participation were evaluated via Box and Block Test, Motor Activity Log (MAL), and Canadian Occupational Performance Measure (COPM), respectively.Results We found significant improvements in impairment (FMA) and participation (COPM) in all groups, however, COPM scores improved beyond the MCID only in the Top-down, and FMA scores exceeded the MCID in Top-down and Bottom-up groups. Use of the upper limb in daily activities (MAL) enhanced in the Top-down group, although was not clinically significant.Conclusion In most of the outcome measures, no significant difference was observed between groups. It seems that Top-down, Bottom-up, and traditional interventions have relatively comparable effectiveness in chronic stroke survivors.Trial Registration IRCT20150721023277N2
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".