The Efficacy and Tolerability of Prostaglandin Analogues in Treating Systemic Sclerosis‐Associated Raynaud Phenomenon: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background: Systemic sclerosis‐associated Raynaud phenomenon (SSc‐RP) confers poor outcomes, including ulceration, gangrene, autoamputation, and hand disability. Prostaglandin analogues (PG) are a group of prostacyclin‐derived drugs with properties that may address underlying complex mechanisms of SSc‐RP. This systematic review and meta‐analysis evaluated the efficacy and tolerability of PGs in SSc‐RP. Methods: We systematically reviewed randomized control trials (RCTs) evaluating PG use in SSc‐RP. The primary outcome was the severity of RP attacks. The secondary outcomes were the frequency and duration of RP attacks, healing of digital ulcers, development of new digital ulcers, change of capillary blood flow, patient health‐reported outcome measure (PROM‐VAS), and tolerability. Results: Eleven RCTs were included, reporting a total of n = 1081 individuals with SSc. PG confers a beneficial effect on RP severity in the short‐term, weighted Mean difference (WMD) −0.63 (95% CI −0.99, −0.27, I2 0%), with no difference in tolerability compared to placebo OR 1.88 (95% CI 1.00, 3.55, I2 = 1%). PG has nonsignificant trends towards improvement in RP frequency WMD of −0.32 (95% CI −0.76, 0.13, I2 = 0%), RP duration WMD −4.78 (95% CI −14.69, 5.14, I2 = 1%), PROM‐VAS WMD −4.81 (95% CI −11.31, 1.69, I2 = 67%), and new or recurrent digital ulcers OR 0.92 (95% CI 0.48, 1.76, I2 = 34%). Conclusion: PGs are beneficial in the short term to reduce the RP severity and are tolerable. Larger, adequately powered trials are needed for higher certainty evidence across SSc‐RP outcomes.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".