Canadian Platform for Trials in Noninvasive Brain Stimulation (CanStim) Consensus Recommendations for Repetitive Transcranial Magnetic Stimulation in Upper Extremity Motor Stroke Rehabilitation Trials
Bibliographic record
Abstract
Objective . To develop consensus recommendations for the use of repetitive transcranial magnetic stimulation (rTMS) as an adjunct intervention for upper extremity motor recovery in stroke rehabilitation clinical trials. Participants . The Canadian Platform for Trials in Non-Invasive Brain Stimulation (CanStim) convened a multidisciplinary team of clinicians and researchers from institutions across Canada to form the CanStim Consensus Expert Working Group. Consensus Process . Four consensus themes were identified: (1) patient population, (2) rehabilitation interventions, (3) outcome measures, and (4) stimulation parameters. Theme leaders conducted comprehensive evidence reviews for each theme, and during a 2-day Consensus Meeting, the Expert Working Group used a weighted dot-voting consensus procedure to achieve consensus on recommendations for the use of rTMS as an adjunct intervention in motor stroke recovery rehabilitation clinical trials. Results . Based on best available evidence, consensus was achieved for recommendations identifying the target poststroke population, rehabilitation intervention, objective and subjective outcomes, and specific rTMS parameters for rehabilitation trials evaluating the efficacy of rTMS as an adjunct therapy for upper extremity motor stroke recovery. Conclusions . The establishment of the CanStim platform and development of these consensus recommendations is a first step toward the translation of noninvasive brain stimulation technologies from the laboratory to clinic to enhance stroke recovery.
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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 | 0.417 | 0.535 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.019 | 0.011 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".