Results of a Pilot Randomized Placebo-Controlled Trial in Primary and Secondary Raynaud's Phenomenon with St. John's Wort: Detecting Changes in Angiogenic Cytokines When RP Improves
Bibliographic record
Abstract
Objectives.To perform a 6-week double-blind RCT in Raynaud's phenomenon (RP) comparing the plant extract St. John's Wort (SJW) to placebo. Methods. RP patients having at least 7 attacks per week were stratified by primary and secondary RP and within secondary by systemic sclerosis or other connective tissue disease. Subjects completed a daily standardized diary recording all RP attacks (frequency, duration and severity). Serum levels of 18 inflammatory and angiogenic cytokines were measured pre- and post-treatment. Results. Eighteen patients completed the study; 8 received SJW and 10 placebo. The decrease in mean number of attacks per day was 0.75 with SJW and 1.01 with placebo, P = 0.06. Attack duration and severity were not different between groups. Cytokine analyses demonstrated no between-groups differences. Combining treatment groups, those with >50% improvement in frequency of attacks yielded a significant increase in E-selectin (P = 0.049), MMP-9 (P = 0.011), G-CSF (P = 0.02), and VEGF (P = 0.012) pre- versus post-treatment. A ≥50% improvement in severity of attacks corresponded to a significant increase in levels of sVCAM-1 (P = 0.003), sICAM-1 (P = 0.007), and MCP-1 (P = 0.004). Conclusions. There were no clinical or biomarker benefit of SJW versus placebo in RP. However, combining all patients, there were changes in some cytokines that may be further investigated.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".