Modeling smoking in systemic sclerosis: A comparison of different statistical approaches
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of different methods of modeling smoking on vascular outcomes in rheumatic diseases. METHODS: Data from the Canadian Scleroderma Research Group Registry were used. Patients self-reported their smoking history. Vascular outcomes were severity of Raynaud's phenomenon, presence of finger ulcers, and severity of finger ulcers. Several models were developed to capture the experience of smoking: 1) ever compared to never smoking; 2) current and past smoking compared to never smoking; 3) never, past, and current smoking compared using polynomial contrasts; 4) smoking intensity, duration, and time since cessation assessed separately; and 5) smoking modeled using the Comprehensive Smoking Index (CSI), which integrates intensity, duration, and time since cessation into a single covariate. RESULTS: This study included 606 patients, of which 16% were current, 42% were past, and 42% were never smokers. Current and past smokers smoked a mean±SD of 25±17 and 17±18 pack-years, respectively. Smoking duration was shorter in past compared to current smokers (18.3 versus 31.7 years). Past smokers reported having stopped smoking approximately mean±SD 16±12 years prior, although this ranged from 1 to 50 years. Smoking had no effect on vascular outcomes in the simplest model comparing ever to never smokers. Models that isolated past smokers revealed the presence of a healthy smoker bias in that group. The model using the CSI demonstrated a strong negative effect of smoking on vascular outcomes. CONCLUSION: Proper modeling of the effect of smoking is essential in studies of vascular outcomes of rheumatic diseases.
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 | 0.063 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".