Brigatinib is another treatment option for patients diagnosed with advanced ALK-positive non-small-cell lung cancer who are treatment-naïve or who have progressed on or are intolerant to crizotinib
Bibliographic record
Abstract
first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing: Column Width: Background: Open AccessArticle Brigatinib is another treatment option for patients diagnosed with advanced ALK-positive non-small-cell lung cancer who are treatment-naïve or who have progressed on or are intolerant to crizotinib by Barbara Melosky *, Parneet Cheema and Geoffrey Liu * Author to whom correspondence should be addressed. Curr. Oncol. 2019, 26(1), 119-120; https://doi.org/10.3747/co.26.4809 Received: 3 November 2018 / Revised: 8 December 2018 / Accepted: 12 January 2019 / Published: 1 February 2019 Download Download PDF Versions Notes Abstract No abstract available Share and Cite MDPI and ACS Style Melosky, B.; Cheema, P.; Liu, G. Brigatinib is another treatment option for patients diagnosed with advanced ALK-positive non-small-cell lung cancer who are treatment-naïve or who have progressed on or are intolerant to crizotinib. Curr. Oncol. 2019, 26, 119-120. https://doi.org/10.3747/co.26.4809 AMA Style Melosky B, Cheema P, Liu G. Brigatinib is another treatment option for patients diagnosed with advanced ALK-positive non-small-cell lung cancer who are treatment-naïve or who have progressed on or are intolerant to crizotinib. Current Oncology. 2019; 26(1):119-120. https://doi.org/10.3747/co.26.4809 Chicago/Turabian Style Melosky, Barbara, Parneet Cheema, and Geoffrey Liu. 2019. "Brigatinib is another treatment option for patients diagnosed with advanced ALK-positive non-small-cell lung cancer who are treatment-naïve or who have progressed on or are intolerant to crizotinib" Current Oncology 26, no. 1: 119-120. https://doi.org/10.3747/co.26.4809 Find Other Styles Article Metrics No No Article Access Statistics For more information on the journal statistics, click here. Multiple requests from the same IP address are counted as one view.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.120 | 0.033 |
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".