Polyarteritis Nodosa: A Systematic Review of Test Accuracy and Benefits and Harms of Common Treatments
Bibliographic record
Abstract
OBJECTIVE: The object of this study was to analyze the benefits and harms of different treatment options and to analyze test accuracy used in the evaluation of patients with primary systemic polyarteritis nodosa (PAN). METHODS: A systematic search of published English-language literature was performed in Ovid Medline, PubMed, Embase, and the Cochrane Library from the inception of each database through August 2019. Articles were screened for suitability in addressing patient, intervention, comparison, and outcome questions, with studies presenting the highest level of evidence given preference. RESULTS: Of 137 articles selected for data abstraction, we analyzed 21 observational studies and seven randomized controlled trials (RCTs). The results showed indirect evidence that a deep skin biopsy provides good diagnostic accuracy. A combined nerve and muscle biopsy should be obtained for patients with PAN with peripheral neuropathy. Cyclophosphamide with high-dose glucocorticoids (GCs) is effective as an induction treatment for newly diagnosed active and severe PAN. GC monotherapy is adequate in the majority of patients with nonsevere PAN, although it has a high relapse rate with GC taper. There was insufficient data in determining the optimal duration of non-GC and GC maintenance therapy. Tumor necrosis factor inhibitors are effective treatment for patients with deficiency of adenosine deaminase 2 (DADA2) with stroke and vasculitis manifestations. CONCLUSION: This comprehensive systematic review synthesizes and evaluates the harms and benefits of different treatment options and the accuracy of commonly used tests for the diagnosis of systemic PAN. Data for diagnosis and management of PAN and DADA2 are mostly limited to observational studies. More high-quality RCTs are needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".