Bibliographic record
Abstract
Lupus (2007) 16, 925‐928 http://lup.sagepub.com Since the first description of the bimodal mortality pattern of systemic lupus erythematosus (SLE) showing that coronary artery disease (CAD) is a significant cause of morbidity and mortality in this condition, multiple studies have addressed the nature of these clinical outcomes and the associated risk factors. However, their exact incidence and prevalence is unknown and the relevant risk factors have not been fully elucidated. A few studies have attempted to identify risk factors for CAD in lupus. 1‐4 Most compared patients with SLE and documented CAD to those with no CAD, addressing classic cardiac risk factors. To date, these studies have found elevated total cholesterol and older age at diagnosis of SLE to be significantly associated with CAD. Other factors implicated in one or two studies have been hypertension, 2,3 previous cardiac involvement with SLE, 3 obesity 2 and longer duration of corticosteroid use. 1 However, lupus populations vary with regard to organ involvement and given the limited power of previous studies, it has not been possible to adequately address these variables in the past.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".