Healthcare Cost and Loss of Productivity in a Canadian Population of Patients with and without Lupus Nephritis
Bibliographic record
Abstract
OBJECTIVE: To compare the healthcare cost and loss of productivity in patients with systemic lupus erythematosus (SLE) with (LN) and without lupus nephritis (lupus nephritis-negative, LNN). METHOD: Patients were classified into those with active (ALN and ALNN) and inactive disease (ILN and ILNN). Patients reported on visits to healthcare professionals and use of diagnostic tests, medications, assistive devices, alternative treatments, hospital emergency visits, surgical procedures, and hospitalizations as well as loss of productivity in the 4 weeks preceding enrollment. RESULTS: Enrollment was 141 patients, 79 with LN and 62 LNN. Patients with LN were more likely to visit rheumatologists and nephrologists, undergo diagnostic tests, and had higher costs for medications than patients who were LNN. The annual healthcare cost averaged $CAN 12,597 ± 9946 for patients with LN and $10,585 ± 13,149 for patients who were LNN, a difference of $2012 (95% CI -$2075, $6100). Patients with ALN had more diagnostic tests and surgical procedures, contributing to a significantly higher annual direct cost ($14,224 ± 10,265) compared to patients with ILN ($9142 ± 8419) and a difference of $5082 (95% CI $591, $9573). The healthcare cost was not different between patients with ALNN and patients with ILNN. In patients with LN and patients who were LNN, < 50% were employed and on average missed 6.5-9 days of work per month. The loss of productivity was significantly higher for caregivers of patients with LN than caregivers of patients who were LNN. CONCLUSION: Healthcare cost and loss of productivity were similar between patients with LN and patients who were LNN; the loss of productivity for caregivers is higher for patients with LN; and the healthcare cost is greater in ALN than in ILN.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".