Guilt by Association: The Challenge of Teasing Apart the Effect of Various Risk Factors for Coronary Heart Disease in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Since the original description over 3 decades ago of the association between atherosclerotic coronary heart disease (CHD) and systemic lupus erythematosus (SLE) by Urowitz, et al 1 many advances have been made in our understanding of the role of traditional, novel, and disease-related risk factors for clinical CHD in SLE2. Esdaile, et al have shown that traditional risk factors such as hypercholesterolemia and hypertension account only partly for the increased risk of CHD in SLE, indicating that disease- and treatment-related factors may also be important3. In the latest installment in the field, published in this issue of The Journal, Haque, et al seek to identify and quantify the role of various risk factors for clinical CHD in SLE, using a case-control design4. They show that patients with clinical CHD are more likely to be male, older, and hypertensive, to have a family history of CHD, to have more “damage” on the Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) damage index, and to have been exposed to azathioprine prior to their CHD event. The “LASER” study highlights several methodological challenges in studies of cardiovascular risk factors in SLE. The first challenge relates to study design. While prospective collection of data on risk factors and CHD outcomes is the ideal model, many studies to date, including the LASER study, have used a retrospective chart review method. Not only does this introduce a possible source of bias, particularly in relation to the temporal association between exposure and outcome, it also means that the dataset may be incomplete for certain key variables. Here the choice and definition of risk factors (“independent variables”) for inclusion in the analysis are also limited to information that is routinely collected and documented in the course of clinical care. In the … Address correspondence to Dr. Nikpour. E-mail: mnikpour{at}medstv.unimelb.edu.au
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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 | 0.206 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.011 | 0.038 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.013 | 0.041 |
| Insufficient payload (model declined to judge) | 0.004 | 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".