176 Accrual of disease comorbidities over 8 years in a multicentre inception sle cohort
Bibliographic record
Abstract
Background and aims The annual accrual of comorbidities in patients with SLE is not well described. We report the annual occurrence of these features in an inception cohort of patients with SLE. Methods An international research network comprised of 33 centres from 11 countries has followed an inception cohort of SLE patients 2yearly according to a standardised protocol between 2000 and 2016. Of these, 717 patients followed for a minimum of 8 years constitute the study population. Comorbidities including atherosclerotic vascular events (AVEs), osteoporosis, osteonecrosis and diabetes are assessed using the SLICC/ACR Damage Index (SLICC/DI). AVEs are described and attributed on a specialised form. Diagnosis of an event is confirmed using standard clinical criteria, relevant laboratory data and imaging where appropriate. Descriptive statistics were used. Results Of the 717 patients followed for at least 8 years, 90.2% were female, 47.3% were Caucasian, 13.8% were Black, 19.4% were Asian, 16.3% Hispanic and 3.2% other. Their mean age at enrolment was 34.2±13.1 years and SLEDAI-2K at enrolment was 4.17±4.49. The duration from diagnosis to enrolment was 5.9±4.4 months. Mean SDI gradually increases over 8 years. The accumulation of AVEs, osteoporosis, osteonecrosis and diabetes all increase progressively over an 8 year period. Caucasians accumulate AVEs and osteoporosis more frequently than all “other” ethnicities. In contrast, all “other” ethnicities accumulate osteonecrosis more frequently than Caucasians. All ethnicities accumulate diabetes at the same frequency. Conclusions As expected disease damage and comorbidities in newly diagnosed patients increase over their first 8 years. Different ethnicities accumulate comorbidities at different rates.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".