Development, validation and results from the impact of treatment evolution in non-small cell lung cancer (iTEN) model
Bibliographic record
Abstract
Objectives Treatment of advanced NSCLC (aNSCLC) is rapidly evolving, as new targeted and immuno-oncology (I-O) treatments become available. The iTEN model was developed to predict the cost and survival benefits of changing aNSCLC treatment patterns from a Canadian healthcare system perspective. This report describes iTEN model development and validation. Materials & methods A discrete event patient simulation of aNSCLC was developed. A modified Delphi process using Canadian clinical experts informed the development of treatment sequences that included commonly used, Health Canada approved treatments of aNSCLC. Treatment efficacy and the timing of progression and death were estimated from published Kaplan–Meier progression free and overall survival data. Costs (2018 CDN$) included were: drug acquisition and administration, imaging, monitoring, adverse events, physician visits, best supportive care, and end-of-life. Results and conclusion Clinical validity of the iTEN model was assessed by comparing model survival predictions to published real-world evidence (RWE). Four RWE studies that reported the overall survival of patients treated with a broad sampling of common aNSCLC treatment patterns were used for validation. The validation coefficient of determination was R 2 = 0.95, with the model generally producing estimates that were neither optimistic nor conservative. The model estimated that current Canadian practice patterns yield a median survival of almost 13 months, a five-year survival rate of 3% and a life-time per-treated-patient cost of $110,806. Cost and survival estimates are presented and were found to vary by aNSCLC subtype. In conclusion, the iTEN model is a reliable tool for forecasting the impact on cost and survival of new treatments for aNSCLC.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".