A comprehensive clinical evaluation of first-line drugs for ALK-positive advanced non-small cell lung cancer
Bibliographic record
Abstract
Background: Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) are mainly used in the treatment of ALK-positive advanced non-small cell lung cancer (NSCLC), but a comprehensive clinical evaluation of ALK-TKIs is lacking. Hence, a comparison of ALK-TKIs for first-line treatment of ALK-positive advanced NSCLC is essential to provide rational drug use and a basis for improving national policies and systems. Methods: According to the Guideline for the Administration of Clinical Comprehensive Evaluation of Drugs (2021) and the Technical Guideline for the Clinical Comprehensive Evaluation of Antitumor Drugs (2022), a comprehensive clinical evaluation index system of first-line treatment drugs for ALK-positive advanced NSCLC was established by literature review and expert interviews. We conducted a systematic literature review, meta-analysis, and other relevant data analyses, combined with an indicator system, to establish a quantitative and qualitative integration analysis for each indicator and each dimension of crizotinib, ceritinib, alectinib, ensartinib, brigatinib, and lorlatinib. Results: The comprehensive clinical evaluation results of all dimensions were as follows: in terms of safety, alectinib had a lower incidence of grade 3 and above adverse reactions; for effectiveness, alectinib, brigatinib, ensartinib, and lorlatinib showed better clinical efficacy, and alectinib and brigatinib have been recommended by several clinical guidelines; in terms of economy, second-generation ALK-TKIs have more cost-utility advantages, and both alectinib and ceritinib have been recommended by the UK and Canadian Health Technology Assessment (HTA) agencies; for suitability, accessibility, and innovation, alectinib has a higher degree of physician recommendations and patient compliance. Except for brigatinib and lorlatinib, all other ALK-TKIs have been admitted to the medical insurance directory; the accessibility of crizotinib, ceritinib, and alectinib is good, meeting the needs of patients. Second- and third-generation ALK-TKIs have higher blood-brain barrier permeability, stronger inhibition ability, and innovation than first-generation ALK-TKIs. Conclusions: Compared with other ALK-TKIs, alectinib performs better across six dimensions and has a higher comprehensive clinical value. The results provide better drug choice and rational use for patients with ALK-positive advanced NSCLC.
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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 | 0.054 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".