Decision Framework for Chemotherapeutic Interventions for Metastatic Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Best supportive care has long been considered to be the standard therapy for metastatic non-small-cell lung cancer (NSCLC). There is now evidence from randomized trials that a number of chemotherapy regimens can palliate cancer-related symptoms and modestly improve survival. We show how cost-effectiveness analyses can be used to make choices between different (ambulatory) chemotherapy regimens. METHODS: Clinical algorithms describing the diagnosis, staging, and treatment of metastatic NSCLC were incorporated into Statistics Canada's Population Health Model. Using consistent methodology, we assessed the cost-effectiveness of several chemotherapeutic interventions: a combination of vindesine (VDS) plus cisplatin, etoposide (VP-16) plus cisplatin, vinblastine (VLB) plus cisplatin, vinorelbine (Navelbine; NVB) plus cisplatin, paclitaxel (Taxol) plus cisplatin, and gemcitabine (GEM) and NVB alone. We calculated the total chemotherapy costs in 1995 Canadian dollars, the cost per case, the average life-years saved, and the cost per life-year saved. Using the Population Health Model, we then constructed an advanced decision framework that rank-ordered the various treatment regimens so as to optimize benefit below various cost-effectiveness thresholds. RESULTS: One regimen (VLB plus cisplatin) appears to result in better survival and lower health care expenditures than best supportive care. By use of cost-effectiveness thresholds of $25,000 and $50,000 per life-year gained, NVB plus cisplatin is the preferred regimen. When quality of life is considered, however, GEM is preferred to NVB plus cisplatin at a threshold value of $50,000. At thresholds of $75 000 and $100,000, paclitaxel plus cisplatin at a dose of 135 mg/m(2) is the preferred regimen. At thresholds of $50,000 and above, best supportive care is the least preferred regimen. CONCLUSIONS: This decision framework allows the comparison of different treatment regimens based on various cost-effectiveness thresholds. Our analysis also supports the use of chemotherapy regimens and the abandonment of best supportive care as the standard of care for patients with advanced NSCLC. [J Natl Cancer Inst 2000;92:1321-9].
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".