Cost-of-Illness Study for Non-Small-Cell Lung Cancer Using Real-World Data
Bibliographic record
Abstract
Background: With recent advances in the treatment of non-small-cell lung cancer (nsclc) and current fiscal constraints within publicly funded health care systems, understanding the real-world economic effect of lung cancer management has become important. The objective of the present study was to determine the costs and resources used in the management of nsclc cohorts in Ontario. Methods: Patients diagnosed between 1 April 2010 and 31 March 2015 were identified in the Ontario Cancer Registry and linked to provincial administrative databases, capturing resources such as hospitalizations, cancer clinic visits, physician services, and systemic therapies or radiotherapy. A cost-of-illness analysis using a bottom-up approach and the GETCOST macro available at ices determined the overall total and mean costs in 2017 Canadian dollars. Resource utilization results were analyzed according to the total number of encounters per resource, the number of patients using each resource, and the number of encounters per patient. A separate cost-and-resource analysis was conducted for radiotherapy. Results: The 24,729 nsclc patients identified included 4542 with stage iii unresectable disease and 10,103 with stage iv nonsquamous disease. The overall total cost for all nsclc patients was $1.9 billion, with inpatient hospitalizations ($635.2 million), cancer clinic visits ($323.7 million), and physician services ($301.4 million) being the top cost contributors. The mean cost per patient was $76,816. The total cost of radiotherapy was $38.5 million. Conclusions: Real-world costs for the management of nsclc during the 5-year period examined were substantial, despite the fact that median survival was poor and treatment information was limited.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".