The CAP-IASLC-AMP molecular testing guideline for the selection of lung cancer patients for EGFR and ALK tyrosine kinase inhibitors.
Bibliographic record
Abstract
11085 Background: The College of American Pathologists (CAP), the International Association for the Study of Lung Cancer (IASLC), and the Association for Molecular Pathology (AMP) jointly initiated an effort to establish evidence-based recommendations for the molecular analysis of lung cancers required to guide EGFR- and ALK-directed therapies, addressing which patients and samples should be tested, and when and how testing should be performed. Methods: Three co-chairs without relevant conflicts of interest were selected, one from each of the sponsoring societies: CAP (P.T.C.), IASLC (M.L.), and AMP (N.I.L.). Writing and advisory panels were formed from additional experts from these societies. Unbiased literature searches were performed to capture articles up to February 2012, yielding 1,533 articles whose abstracts were screened to identify 521 pertinent articles that were then reviewed in detail for relevance. Evidence was formally graded for each of the recommendations first formulated by the co-chairs and panel members at a public meeting. Each guideline section was assigned to at least two panelists. Successive drafts were circulated for comments to the writing panel, the advisory panel, the public (online posting), and the three professional societies. Results: We generated 37 guideline items addressing 14 areas of EGFR and ALK testing. The major, evidence-based recommendations are to test for EGFR mutations and ALK fusions in all patients with advanced stage adenocarcinoma, regardless of sex, race, or smoking history, and to prioritize EGFR and ALK testing over other molecular predictive tests. Recommendations and expert consensus opinions were generated for all other key aspects of EGFR and ALK testing in lung cancer related to oncology and pathology practice and technical issues in molecular testing. Conclusions: As scientific discoveries and clinical practice outpace the completion of randomized clinical trials, evidence-based guidelines developed by expert practitioners are vital for communicating emerging clinical standards and thereby improving patient outcomes.
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 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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".