Smoking Status over Two Years in Patients with Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Smoking increases the risk of multiple sclerosis (MS) and possibly disease progression. The reliability of self-reported smoking status is unknown in MS. We assessed the reliability of self-reported smoking status among participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. METHODS: In 2004 and 2006, NARCOMS participants reported smoking status using Behavioral Risk Factor Surveillance Survey questions. We compared responses from 5,458 participants answering both questionnaires. We measured agreement regarding smoking status (ever/current) using a kappa coefficient, and agreement for ages of starting and quitting smoking, and number of cigarettes smoked using an intraclass correlation coefficient (ICC). RESULTS: In 2004, 2,885 (53.4%) participants reported ever smoking. The kappa coefficient for ever smoking was 0.90 (95% confidence interval, CI: 0.89-0.92) and for current smoking 0.92 (95% CI: 0.90-0.94). The ICC for age at starting smoking was 0.73 (95% CI: 0.71-0.75) and for age at quitting smoking 0.90 (95% CI: 0.89-0.91). African-Americans, younger participants and those of lower socioeconomic status were less reliable. Depressed participants reported current smoking status less consistently (odds ratio: 0.51; 95% CI: 0.39-0.67). CONCLUSIONS: NARCOMS participants reliably report smoking status. The impact of depression on reliability of self-reported smoking status needs re-evaluation.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".