Barriers to patient recruitment in a poststroke neurorehabilitation multicenter trial in Brazil
Bibliographic record
Abstract
There is a high demand for stroke rehabilitation in the Brazilian public health system, but most studies that have addressed rehabilitation for unilateral spatial neglect (USN) after stroke have been performed in high-income countries. Therefore, the aim of this study was to analyze USN patient recruitment in a multicenter noninvasive brain stimulation clinical trial performed in Brazil and to provide study design recommendations for future studies. We evaluated the reasons for exclusion of patients from a multicenter, randomized, double-blinded clinical trial of rehabilitation of USN patients after stroke. Clinical and demographic variables were compared between the included and excluded patients. A descriptive statistical analysis was performed. Only 173 of the 1953 potential neglect patients (8.8%) passed the initial screening. After screening evaluation, 87/173 patients (50.3%) were excluded for clinical reasons. Cognitive impairment led to the exclusion of 21/87 patients (24.1%). Low socioeconomic status led to the exclusion of 37/173 patients (21.4%). Difficulty obtaining transportation to access treatment was the most common reason for their exclusion (16/37 patients, 43.3%). The analyzed Brazilian institutions have potential for conducting studies of USN. The recruitment of stroke survivors with USN was restricted by the study design and limited financial support. A history of cognitive impairment, intracranial stenting or craniectomy, and lack of transportation were the most common barriers to participating in a multicenter noninvasive brain stimulation trial among patients with USN after stroke.
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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 | 0.137 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".