Bibliographic record
Abstract
Objectives To evaluate the annual direct medical costs and the impact of SLE disease severity and flares on incremental costs in autoantibody positive SLE patients (pts) managed by specialists. Methods A retrospective study was conducted in three Canadian academic medical centers with established cohorts of SLE patients. Data on patient characteristics, disease activity and severity, and medical resource utilization were collected through chart review. Consecutive pts seen in clinic between July 2007 and June 2008 were screened for SLE flares and disease severity using predefined decision rules for both. The number and proportion of pts with severe and non-severe SLE was also defined a priori. Patients who met the inclusion criteria were stratified by disease severity (severe and non-severe active SLE) and followed for 2 yrs (±6 mths after the inclusion visit). Severe disease was defined as involvement of renal, neurological, cardiovascular or respiratory systems which required >7.5 mg/day of corticosteroids and/or immunosuppressants or any SLE manifestation that required at least 30mg/day of corticosteroids at the inclusion visit. A modified SELENA-SLEDAI Flare Index was used to identify mild/moderate and severe flares. Costs were calculated by multiplying each health resource utilized (i.e., lab and imaging tests, biopsies, meds, specialist visits, day hospitalizations, emerg visits, inpatient and rehab stays) by its corresponding CAD unit cost. Total unadjusted mean costs and costs associated with flares were assessed over 2 yrs and expressed in CAD dollars. The analyses were primarily performed using appropriate descriptive statistics for continuous and categorical data. Multiple regression analyses was used to identify the association between 2 yr costs and number of mild/moderate flares and number of severe flares, adjusting for age and SLICC/ACR damage index score. Results A total of 109 pts, 93.6% female, with a mean (SD) age of 41.4 (±15.4) yrs and mean disease duration of 11.9 (±12.6) yrs were studied. At enrollment, 56 pts had severe active SLE and 53 had non-severe active SLE. The mean number of flares for severe and non-severe pts over the study period was 2.68 and 1.91 respectively (p=0.005). Patients in the severe patient group had a higher number of severe flares compared to pts with non-severe SLE (1.82 vs. 0.70; p<0.001), while the mild/moderate flare rate did not differ significantly (0.86 vs. 1.21; p=0.063). The average annual direct medical costs were $9,871 and were significantly higher for pts with severe disease compared to those with non-severe SLE ($14,172 vs. $5,326; p<0.001). The average annual direct costs for pts with at least one flare were $10,544 compared to $5,144 (p<0.001) for pts without flares. Regression analysis showed a mean incremental cost of $5,563 for a severe flare, but no significant incremental cost with mild/moderate flares when adjusted for other variables in the model. Conclusions Patients with severe active SLE have 2.7 times higher annual costs compared to pts with non-severe disease. Patients experiencing at least one flare incurred 2 times more costs annually than those without flares. Direct healthcare costs in Canada are influenced by SLE disease severity in addition to the type and frequency of SLE flares. Disclosure of Interest A. Clarke Consultant for: the study and received funds from GSK/HGS, is a consultant for MedImmune and Bristol Myers Squibb, and received research grants from GSK and funds from GSK/HGS for the writing of this abstract. M. Urowitz Consultant for: the study and received funds from GSK/HGS, is a consultant for UCB, Merck/Serono, and received funds from GSK/HGS for the writing of this abstract., N. Monga Employee of: GlaxoSmithKline, N. Topors Shareholder of: GlaxoSmithKline, Employee of: GlaxoSmithKline, J. Hanly Consultant for: the study and received funds from GSK/HGS, received a grant from GSK, and received funds from GSK/HGS for the writing of this abstract.
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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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".