Real-World Costing Analysis for Diffuse Large B-Cell Lymphoma in British Columbia
Bibliographic record
Abstract
Introduction: Diffuse large B-cell lymphoma (dlbcl) accounts for 30%-40% of all non-Hodgkin lymphomas. Approximately 60% of patients are cured with standard treatment. Targeted treatments are being investigated and might improve disease outcomes; however, their effect on cancer drug budgets will be significant. For the present study, we conducted an analysis of real-world costs for dlbcl patients treated in British Columbia, useful for health care system planning. Methods: Patient records from a retrospective cohort of patients diagnosed with dlbcl in British Columbia during 2004-2013 were anonymously linked across multiple administrative data sources: systemic therapy, radiotherapy, hospitalizations, oncologist services, outpatient medications, and fee-for-service physician services. Using generalized linear modelling regression, time-dependent costs (in 2015 Canadian dollars) were estimated in 6-month intervals over a 5-year period. The inverse probability weighting method was applied to account for censored observations. Nonparametric bootstrapping was used to estimate standard errors for the mean cost at each time interval. Results: The cohort consisted of 678 patients (5-year overall survival: 67%). Mean age at diagnosis was 64 ± 14 years; median follow-up was 3.2 years. Mean total cost of care was highest in the first 6 months after diagnosis ($29,120; 95% confidence interval: $28,986 to $29,170) and after disease progression ($18,480; 95% confidence interval: $15,187 to $24,772). Systemic therapy and hospitalization costs were the largest cost drivers. At each time interval, costs were observed to be positively skewed. Conclusions: Our results depict real-world costs for the treatment of dlbcl patients with standard chop-r therapy. Cost-model parameters are also provided for economic modelling of dlbcl interventions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".