EXECUTIVE SUMMARY Context and Policy Issues
Bibliographic record
Abstract
Lung cancer is the second most common cancer in Canada and the leading cause of cancerrelated death. Non-small cell lung cancer (NSCLC) accounts for over 80 % of all lung cancer cases. Some cases of NSCLC are associated with an over-expression of the protein epidermal growth factor receptor (EGFR). Over-expression of EGFR is linked to a more aggressive disease and a poor prognosis. Treatment protocols for NSCLC are evolving to include emerging therapies such as EGFR tyrosine kinase inhibitors (TKIs). Mutations in the EGFR gene have been identified and proposed to be associated with high responsiveness to TKI treatment. This report focuses on the cost-effectiveness of polymerase chain reaction (PCR)-based methods used to detect the presence of EGFR mutations in patients with advanced NSCLC. The report provides an economic framework to guide future economic evaluations of EGFR mutation analysis, particularly focusing on assessing cost-effectiveness of EGFR mutation analysis for the first-line use of gefitinib for the treatment of advanced NSCLC. Disclaimer: This document is prepared by the Health Technology Inquiry Service (HTIS), an information service of the Canadian Agency for Drugs and Technologies in Health. The service is provided to those involved in planning and providing health care in Canada. HTIS responses are based
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.020 | 0.011 |
| Insufficient payload (model declined to judge) | 0.090 | 0.013 |
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".