The cost of treating relapsed indolent non-Hodgkin's lymphoma in an international setting: retrospective analysis of resource use.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Few economic data exist on the treatment of indolent non-Hodgkin's lymphoma (NHL) and there are none in the published literature concerning relapsed disease. This international analysis (Canada, Germany, Italy) was established to estimate the overall direct cost of treating patients with relapsed indolent NHL and determine the main cost components of treatment. DESIGN AND METHODS: Telephone interviews were used to identify the most commonly used treatment regimens in each country. CHOP, CVP and fludarabine were chosen for economic analysis, which was based on retrospective data from 424 patients. RESULTS: Overall treatment costs for a course of six cycles varied more than 5-fold, from 3,445 to 17,940 Euros between regimens and countries. The treatment setting had a major impact on costs, with in-patient costs being up to three times greater than the equivalent out-patient values. Drug administration costs comprised 46-60% of the overall treatment costs in the in-patient setting. Adverse event management was the major cost component for out-patient CHOP and CVP therapy (52-75%), and a significant proportion (24-40%) of in-patient costs for these regimens. Drug acquisition accounted for less than half of treatment costs for most of the regimens analyzed. INTERPRETATION AND CONCLUSIONS: This study shows that not simply drug acquisition costs, but the costs of drug administration, particularly in the in-patient setting, and adverse event management are major contributors to the overall treatment costs for relapsed indolent NHL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".