<scp>N</scp>ational <scp>W</scp>orking <scp>G</scp>roup <scp>M</scp>eeting on <scp>ALK</scp> diagnostics in lung cancer
Bibliographic record
Abstract
The global landscape of molecular testing is rapidly changing, with the recent publication of the International Association for the Study of Lung Cancer (IASLC)/College of American Pathologists (CAP) guidelines and the ALK Atlas. The IASLC/CAP guidelines recommend that tumors from patients with non-small cell lung cancer (NSCLC) be tested for ALK rearrangements in addition to epidermal growth factor receptor (EGFR) mutations. The spur for this recommendation is the availability of novel therapies that target these rearrangements. This article is based on coverage of a Pfizer-sponsored National Working Group Meeting on ALK Diagnostics in Lung Cancer, held around the 15th World Lung Cancer Conference, in Sydney on October 31, 2013. It is based on the presentations given by the authors at the meeting and the discussion that ensued. The content for this article was discussed and agreed on by the authors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.025 |
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".