The effect of theta-burst stimulation on unilateral spatial neglect following stroke: a systematic review
Bibliographic record
Abstract
OBJECTIVE:: To evaluate the effectiveness of theta-burst stimulation for the treatment of stroke-induced unilateral spatial neglect. DATA SOURCES:: A systematic literature search was conducted from the inception of each database to 30 June 2018 using CINAHL, EMBASE, PubMed, PsycINFO, and Scopus. REVIEW METHODS:: Articles were included if theta-burst stimulation was used to treat neglect following a stroke. The additional a priori inclusion criteria were as follows: (1) ⩾3 adult (⩾18 years) participants, (2) ⩾50% stroke population, and (3) peer-reviewed journal articles published in English. Extracted data included study and treatment characteristics, results, and adverse events. RESULTS:: Nine studies met the inclusion criteria, generating a total of 148 participants. Eight studies evaluated a continuous stimulation protocol and one study investigated an intermittent stimulation protocol. Overall, both protocols significantly improved neglect severity when compared against placebo or active controls ( P < 0.05). Adding smooth pursuit training to theta-burst stimulation did not improve neglect relative to when the stimulation was delivered alone ( P > 0.05). There was inconsistent reporting of neglect terminology, outcome measures, and adverse events. The treatment characteristics were heterogeneous among the trials. CONCLUSION:: This systematic review found that theta-burst stimulation seems to improve post-stroke unilateral spatial neglect, but because the evidence is limited to a few small studies with varied and inconsistent protocols and use of terminology, no firm conclusion on effectiveness can be drawn.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".