Comprehensive rehabilitation with integrative medicine for subacute stroke: A multicenter randomized controlled trial
Bibliographic record
Abstract
To determine whether integrative medicine rehabilitation (IMR) that combines conventional rehabilitation (CR) with acupuncture and Chinese herbal medicine has better effects for subacute stroke than CR alone, we conducted a multicenter randomized controlled trial that involved three hospitals in China. Three hundred sixty patients with subacute stroke were randomized into IMR and CR groups. The primary outcome was the Modified Barthel Index (MBI). The secondary outcomes were the National Institutes of Health Stroke Scale (NIHSS), the Fugl-Meyer Assessment (FMA), the mini-mental state examination (MMSE), the Montreal Cognitive Assessment (MoCA), Hamilton's Depression Scale (HAMD), and the Self-Rating Depression Scale (SDS). All variables were evaluated at week 0 (baseline), week 4 (half-way of intervention), week 8 (after treatment) and week 20 (follow-up). In comparison with the CR group, the IMR group had significantly better improvements (P < 0.01 or P < 0.05) in all the primary and secondary outcomes. There were also significantly better changes from baseline in theses outcomes in the IMR group than in the CR group (P < 0.01). A low incidence of adverse events with mild symptoms was observed in the IMR group. We conclude that conventional rehabilitation combined with integrative medicine is safe and more effective for subacute stroke rehabilitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".