Executive summary of the American Radium Society appropriate use criteria for brain metastases in epidermal growth factor receptor mutated-mutated and ALK-fusion non-small cell lung cancer
Bibliographic record
Abstract
The American Radium Society (ARS) Central Nervous System (CNS) committee reviewed literature on epidermal growth factor receptor mutated (EGFRm) and ALK-fusion (ALK+) tyrosine kinase inhibitors (TKIs) for the treatment of brain metastases (BrMs) from non-small cell lung cancers (NSCLC) to generate appropriate use guidelines addressing use of TKIs in conjunction with or in lieu of radiotherapy (RT). The panel developed three key questions to guide systematic review: can radiotherapy be deferred in patients receiving EGFR or ALK TKIs at (1) diagnosis or (2) recurrence? Should TKI be administered concurrently with RT (3)? Two literature searches were performed (May 2019 and December 2023). The panel developed 8 model cases and voted on treatment options using a 9-point scale, with 1-3, 4-6 and 7-9 corresponding to usually not appropriate, may be appropriate, and usually appropriate (respectively), per the UCLA/RAND Appropriateness Method. Consensus was achieved in only 4 treatment scenarios, all consistent with existing ARS-AUC guidelines for multiple BrM. The panel did not reach consensus that RT can be appropriately deferred in patients with BrM receiving CNS penetrant ALK or EGFR TKIs, though median scores indicated deferral may be appropriate under most circumstances. Whole brain RT with concurrent TKI generated broad disagreement except in cases with 2-4 BrM, where it was considered usually not appropriate. We identified no definitive studies dictating optimal sequencing of TKIs and RT for EGFRm and ALK+ BrM. Until such studies are completed, the committee hopes these cases guide decision- making in this complex clinical space.
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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 | 0.041 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".