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Record W2130603905 · doi:10.1200/jco.2014.56.2157

Targeted Therapies in Non–Small-Cell Lung Cancer Management: No Cost-Effective Strategies?

2014· letter· en· W2130603905 on OpenAlexaboutno aff
C. Chouaïd, Isabelle Borget, A. Vergnenègre

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typeletter
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLung cancerOncologyIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

TO THE EDITOR: Using a Markov model and from the Canadian perspective, Dajlalov et al 1 reported the cost-effectiveness in advanced nonsquamous non–small-cell lung cancer (NSCLC) of EML4-ALK fusion testing in combination with targeted first-line crizotinib as well as the cost-effectiveness of first-line crizotinib compared with standard care in patients with known EML4-ALK positivity. The incremental costeffectiveness ratio (ICER) of these two strategies was Canadian $255,870 and $250,632, respecfively, per quality-adjusted life-year (QALY) gained, and the authors concluded that these strategies are not cost effective. Although the authors appropriately discussed the limitations of the study we have two general comments on this exciting topic. Modeling in economic analysis is a useful and a relevant way to obtain results in case when no head-to-head trials have been performed, but specific heed needs to be paid to the key assumptions supporting the model. In the analysis by Dajlalov et al, 1 the standardcare strategy was cisplatin-gemcitabine doublet as first-line therapy, with pemetrexed and erlotinib as second- and third-line therapies. But the recommendations for first-line treatment of nonsquamous NSCLC also included pemetrexed concurrently with cisplatin and as an alternative, according to patient choice, bevacizumab combined with a paclitaxel-carboplatin regimen. Maintenance with bevacizumab or pemetrexed and switch maintenance with pemetrexed until progression are also acceptable options. Taking these recommendations into account impacts the standard of care, especially in costs, which increase from Canadian $1,527 to nearly $8,000 per chemotherapy cycle when using pemetrexed or bevacizumab in the first-line as well the maintenance setting. Unfortunately, in the sensitivity analysis, the cost of standard-care chemotherapy was not tested. Even if only a small proportion of patients received these high-cost drugs, it will probably significantly impact the ICER of both tested strategies. We also have a general comment about the interpretation of the ICER and the discrepancy between our clinical experience with crizotinib in patients with EML4-ALK positive NCSLC and the disappointing results of the economic analysis. In fact, these results should be compared with the ICER of recent strategies for management in NSCLC. For nonsquamous NSCLC in the United States in first-line setting, 2 the ICER of treatment with a cisplatin-pemetrexed doublet compared with carboplatin-paclitaxel and the ICER of treatment with carboplatin-paclitaxel-bevacizumab compared with cisplatinpemetrexed were $178,613 and $300,000, respectively, per life-year gained (LYG)—approximately $340,000 and $566,000, respectively, per QALY. Using pemetrexed as maintenance therapy 3 compared with observation led to an ICER of $122,371 per LYG (approximately $230,000 per QALY). In second-line setting in Canada, the ICER for erlotinib treatment in the

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.014
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.084
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0040.001
Research integrity0.0080.016
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.052
GPT teacher head0.470
Teacher spread0.418 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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