Evaluation of Subcutaneous Rituximab Administration on Canadian Systemic Therapy Suites
Bibliographic record
Abstract
Background: Non-Hodgkin lymphoma (nhl) is the most common hematologic malignancy. Diffuse large B-cell lymphoma (dlbcl) and follicular lymphoma (fl) constitute 55% of new nhl cases and are initially treated with rituximab-based chemoimmunotherapy. Relative to intravenous (IV) rituximab, a subcutaneous (sc) formulation approved in 2016 has comparable pharmacokinetics, efficacy, and safety, and a greatly reduced administration time; it is also preferred by patients. The objective of the present study was to estimate the effect (on systemic therapy suite time and on the costs of drug acquisition and administration) of implementing sc rituximab in the initial chemoimmunotherapy for fl and dlbcl over 3 years in the Canadian market. Methods: An Excel (Microsoft Corporation, Redmond, WA, U.S.A.)-based model was created with a population size based on epidemiologic data and current rituximab use, duration of use considering initial therapy, time savings for sc rituximab administration from published studies, costs from standard Canadian sources, and assumed uptake in implementing provinces of 65%, 75%, and 80% over 3 years. Key parameters and sensitivity analysis values were validated by clinical experts located in various Canadian jurisdictions. Costs are reported in 2017 Canadian dollars from the perspective of the health care system. Results: More than 3 years after implementation of sc rituximab, we estimated that 5762 Canadians would be receiving sc rituximab, resulting in savings of 128,715 hours in systemic therapy suite time and approximately $40 million in drug and administration costs. Sensitivity analyses suggest that the model is most sensitive to sc market uptake, number of induction therapy cycles, and eligible patients. Conclusions: Subcutaneous administration of rituximab can significantly reduce systemic therapy suite time and achieve substantial savings in drug and administration costs.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".