Systemic Lupus Erythematosus in 6 Male Cocaine Users at Bellevue Hospital: Table 1.
Bibliographic record
Abstract
To the Editor: Genetics, environment, and gender contribute to the pathogenesis of systemic lupus erythematosus (SLE). As in other autoimmune diseases, females are more often afflicted than males, with a ratio of 9:11. Sex hormones themselves have been held responsible for this imbalance, since SLE often flares during pregnancy and tends to remit after menopause2,3. The effect of cocaine on innate and acquired immunity has been extensively investigated. While cocaine modifies immune responses and is linked to an increased susceptibility to infections4, no association between cocaine use and the induction of SLE has been described. We describe 6 cases treated at the New York University Medical Center Bellevue Hospital, an institution often characterized as the “canary in the coal mine” of urban medicine5. In this tradition, we describe 6 men with SLE with chronic cocaine abuse and suggest possible mechanisms to explain an association between cocaine and SLE6. Patients’ characteristics are detailed in Table 1. Patient 1, a 25-year-old Hispanic man with a history of polysubstance abuse, including cocaine, was diagnosed with lupus nephritis that progressed to endstage renal disease … Address correspondence to Dr. T.L. Rivera, Department of Medicine, Division of Rheumatology, New York University School of Medicine, 301 East 17th Street, Room 1410, New York, NY 10003. E-mail: tania.rivera{at}nyumc.org.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".