Cigarette Smoking and Cutaneous Damage in Systemic Lupus Erythematosus: Table 1.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between cigarette smoking and cutaneous damage in systemic lupus erythematosus (SLE). METHODS: Our study was performed in SLE clinic registry cohort patients, all of whom fulfilled revised American College of Rheumatology criteria for SLE; patients are followed prospectively with annual assessments that include collection of demographic variables, smoking history, disease activity (SLE Disease Activity Index version 2000, SLEDAI-2K), medications, and damage scores (Systemic Lupus International Collaborating Clinics/ACR Damage Index). Cumulative cutaneous damage scores were used for the primary analyses. Logistic and logit regression models were performed to examine potential associations between current smoking and cutaneous damage, controlling for age, sex, race, lupus disease duration, antimalarial or immunosuppressant use, and anti-DNA and anti-SSA antibody status. RESULTS: Of our sample (N = 276), 92% were women and 73.7% were Caucasian; the mean age was 45.1 years, mean disease duration 13.5 years, and 17.5% were current smokers. In the regression analyses, current cigarette smoking was associated with total cutaneous damage (OR 2.73, 95% CI 1.10, 6.81) and with scarring (OR 4.70, 95 CI 1.04, 21.2). In additional analyses, current smoking was also associated with active lupus rash (OR 6.18, 95% CI 1.63, 23.3). CONCLUSIONS: Current cigarette smoking may be associated with cutaneous damage and active lupus rash in SLE, suggesting another reason to emphasize smoking cessation in patients with SLE.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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".