Demographic, Clinical, and Immunologic Correlates among a Cohort of 50 Cocaine Users Demonstrating Antineutrophil Cytoplasmic Antibodies
Bibliographic record
Abstract
OBJECTIVE: Cocaine/levamisole-associated autoimmunity syndrome (CLAAS) is a poorly understood form of drug-induced autoimmunity. Our goals were to better characterize the spectrum of clinical and immunologic features of CLAAS, to identify demographic risk factors, and to generate new hypotheses regarding pathogenesis. METHODS: CLAAS subjects were identified between 2001 and 2015 at the University of Washington Medical Center, Harborview Medical Center, and affiliated clinics in Seattle, Washington, USA. Demographic, clinical, and immunologic variables were collected and correlated using contingency and logistic regression analyses. We used similar analyses to compare CLAAS subjects with all individuals exhibiting antineutrophil cytoplasmic antibodies (ANCA+) or cocaine use (Cocaine+) in an associated deidentified clinical data repository. RESULTS: We identified 50 CLAAS subjects. Compared to all Cocaine+ individuals (n = 2740), CLAAS subjects were more likely to be female and less likely to self-identify as black/African American. CLAAS subjects showed several ANCA patterns, including anti-MPO (myeloperoxidase)/anti-PR3 (proteinase 3) dual reactivity, a finding that appears to be specific to CLAAS. Hematologic, renal, and skin abnormalities were most frequently reported, including neutropenia and skin purpura. Finally, we observed strong, independent associations between the cytoplasmic ANCA (C-ANCA) pattern and mortality. CONCLUSION: We identify sex and race as important risk modifiers in the developing CLAAS among cocaine users. The development of C-ANCA was associated with increased mortality. Moreover, we confirm the enriched presence of anti-MPO/anti-PR3 dual reactivity in CLAAS, further supporting the diagnostic utility of this feature.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".