Glomerular filtration rate predicts arterial events in women with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: To determine whether renal function predicts the development of cardiovascular disease and other arterial vascular events in patients with SLE. METHOD: An inception cohort of 437 females was studied. Baseline estimated glomerular filtration rate (eGFR) was calculated using serum creatinine and the abbreviated Modification of Diet in Renal Disease Study Group formula. Arterial events including myocardial infarction, angina, transient ischaemic attacks, cerebral vascular accidents and other arterial events were documented at up to 15 years since the first visit. Disease activity was determined using SLEDAI. Patients were classified into those with or without arterial events and further into events that occurred within or after 3 years since the first visit (events <3 years, events ≥3 years). The association between eGFR and risks of arterial events was investigated using the Cox proportional hazards model. RESULTS: There was a total of 58 arterial events of which 51.9% were events ≥ 3 years. Patients with arterial events had a significantly lower baseline eGFR and were significantly older than those without arterial events. Furthermore, baseline eGFR was significantly lower in events <3 years compared with events ≥ 3 years. Baseline eGFR, age and baseline SLEDAI were significantly associated with the risks of arterial events [eGFR: hazard ratio (HR) = 0.986; age: HR = 1.032; SLEDAI: HR = 1.041]. CONCLUSION: Lower baseline eGFR, older age and higher SLEDAI score were significantly associated with increasing odds of developing arterial events at an earlier stage of SLE.
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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".