Colchicine for the Primary and Secondary Prevention of Pericarditis: An Update
Bibliographic record
Abstract
OBJECTIVE: To review the efficacy and safety of colchicine as primary and secondary prophylaxis for pericarditis. DATA SOURCES: We searched MEDLINE, EMBASE, PubMed, BIOSIS Previews, International Pharmaceutical Abstracts, Web of Science, and CENTRAL for controlled studies from database inception date to July 2009. Search terms included colchicine, pericarditis, and postpericardiotomy syndrome (PPS). STUDY SELECTION AND DATA EXTRACTION: Prospective, randomized, controlled trials investigating the use of colchicine in preventing pericarditis were included. Data extracted included design, inclusion criteria, demographics, interventions, background therapy, and pericarditis-related clinical outcomes. DATA SYNTHESIS: Data were synthesized qualitatively, given variable study designs. Three trials were identified. A single trial examining primary prevention evaluated the use of colchicine versus placebo for preventing PPS in patients undergoing cardiopulmonary bypass grafting. No significant reduction in PPS was found. Two studies examined secondary prevention of pericarditis, comparing colchicine plus aspirin versus aspirin alone. One study examined using these comparators to treat a first episode of pericarditis. After 3 months, there was a significant reduction in recurrent pericarditis with colchicine plus aspirin (11.7% vs 33%; p = 0.009). Another study examined this same regimen in recurrent pericarditis, finding a significant reduction in recurrence after 6 months (21% vs 45%; p = 0.02). CONCLUSIONS: Despite limitations in study designs, current evidence suggests a role for colchicine in the secondary prophylaxis for recurrent pericarditis. The evidence for use of colchicine as primary prophylaxis in PPS is indeterminate; therefore, colchicine cannot be recommended routinely. While colchicine should be recommended for the prevention of recurrent pericarditis, questions regarding the optimal regimen and long-term safety profile need to be further elucidated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".