Evaluating the Safety and Effectiveness of Perispinal Etanercept for Post-Stroke Recovery: A Systematic Review
Bibliographic record
Abstract
Stroke remains a leading cause of long-term disability in adults, with many survivors experiencing persistent neurological and functional deficits. Conventional rehabilitation offers limited benefit once recovery plateaus. Biological therapies targeting chronic mechanisms of neural dysfunction, such as perispinal etanercept (PSE), a tumor necrosis factor-alpha (TNF-α) inhibitor, have been proposed as potential alternatives. This systematic review evaluates the safety and effectiveness of PSE for neurological recovery in adults with chronic post-stroke deficits. A comprehensive search of PubMed, Embase, MEDLINE, Cochrane Library, Scopus, and Web of Science identified human studies assessing PSE in stroke ≥six months post-event. Two reviewers independently screened, extracted data, and assessed bias using the risk of bias 2 (RoB 2), Newcastle-Ottawa Scale, and Joanna Briggs Institute (JBI) tools. Eligible studies included randomised trials, non-randomised trials, and case-based reports describing neurological, functional, cognitive, or safety outcomes. Five studies met the inclusion criteria: two randomised controlled trials, one large observational cohort, one case series, and one case report. The available evidence showed variable outcomes. Some studies reported short-term improvements in pain and motor function, whereas others found no significant overall functional or quality-of-life gains. Reported benefits, when observed, were typically domain-specific and not consistently sustained. Treatment was well tolerated, with no major safety concerns reported. Robust, large-scale trials are still needed to establish consistent functional benefits and long-term efficacy. Future research should focus on optimising treatment protocols and identifying responsive patient phenotypes through neuroimaging and inflammatory biomarkers. Well-designed multicenter trials with standardised stroke-specific endpoints and extended follow-up are essential to determine the true therapeutic value of PSE before routine clinical adoption.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".