091. Are Cardiac Risk Profiles of Early Rheumatoid Arthritis Patients Addressed at Clinic?
Bibliographic record
Abstract
Background: RA patients have higher cardiovascular disease (CVD) risk scores compared with the general population. It has been reported that rheumatologist feel CVD risk management is the responsibility of primary care providers (PCP). This situation creates a serious care gap as the CVD risk/management is left to the patients’ PCP who may not be comfortable managing these clinical issues in complex patients. Our objective was to determine how CVD risk was addressed by rheumatologists. Methods: Ethics approval from the University of Calgary Conjoint Health Research Ethics Board was granted to complete a retrospective chart review on randomly selected patients who were referred to our Early Inflammatory Arthritis (EIA) Clinic between January 2009 and December 2012. Demographic data was recorded on all patients at baseline. The following variables were collected at baseline, 6 month and 12 month visits: DAS for 28 joints (DAS28)-ESR, all medications, traditional CVD risk factors, and diagnoses of CVD. We calculated Framingham Risk Scores (FRS) based on lipids and BMI and QRISK2 scores. We also extracted the rheumatologists’ recommendations for CVD risk reduction.
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 | 0.005 |
| 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) | 0.005 | 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".