Resource utilization and direct medical costs in adult systemic lupus erythematosus patients from a commercially insured population
Bibliographic record
Abstract
OBJECTIVE: Our aim was to estimate annual health care resource use and medical costs associated with systemic lupus erythematosus (SLE) in a large US managed care health plan. METHODS: Subjects at least 18 years of age and with claims-based evidence of SLE (ICD-9-CM 710.0x) were identified from a health plan database. Subjects were matched on the basis of demographic and clinical characteristics to unaffected controls. Resource use and costs were determined during a fixed 12-month period. A generalized linear model (GLM) was used to adjust costs for demographic and clinical characteristics. RESULTS: In total, 1278 newly diagnosed SLE subjects were matched to 3834 controls, and 10,152 subjects with existing SLE were matched to 30,456 controls. Health care resource use was significantly higher among SLE subjects than matched controls, including average annual numbers of ambulatory visits, specialist visits, and inpatient hospital stays (all p < 0.001). SLE subjects had significantly higher overall mean annual medical costs than matched controls (newly diagnosed: $19,178 vs. $4909; existing: $15,487 vs. $5156; both p < 0.001). Evidence of specific organ involvement including renal failure and central nervous system complications, were each associated with increased costs (both p < 0.001). CONCLUSIONS: Subjects with SLE have high resource use and medical costs relative to controls.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".