A Meta-Analysis and Meta-Regression of Frequency and Risk Factors for Poststroke Complex Regional Pain Syndrome
Bibliographic record
Abstract
Background and Objectives: This article aimed to investigate the risk factors for poststroke complex regional pain syndrome (CRPS). Materials and Methods: We searched electronic databases including PubMed, Medline, Web of Science, Cochrane Library, and Embase up to 27 October 2021. We enrolled analytical epidemiological studies comprising cohort, case-control, and cross-sectional studies. A quality assessment was performed using the Newcastle–Ottawa Quality Assessment Scale for cohort and case-control studies and the Joanna Briggs Institute critical appraisal checklist for analytical cross-sectional studies. Binary outcomes were reported as odds ratios (ORs), and continuous outcomes were described as standardized mean differences (SMDs) with 95% confidence intervals. For the meta-regression, beta coefficient and p value were adopted. Results: We included 21 articles comprising 2225 participants. Individuals with shoulder subluxation and spasticity were found to have higher risks for poststroke CRPS. Spasticity with higher modified Ashworth scale score, lower Brunnstrom hand stage, and inferior Barthel index scores were observed in patients with poststroke CRPS. The pooled incidence proportion in nine articles was 31.7%, and a correlation was found between effect sizes and the ratio of women and the proportion of left hemiparesis. The summarized prevalence in nine cross-sectional studies was 33.1%, and a correlation was observed between prevalence and the subluxation ratio and Brunnstrom stage. Conclusions: Based on our meta-analysis, being female, left hemiparesis, shoulder subluxation, spasticity, a lower Brunnstrom stage of distal upper limb, and an inferior Barthel index are all features for poststroke CRPS. Larger studies with greater statistical power may confirm our findings and clarify some other unknown risk factors for poststroke CRPS.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.076 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".