Recommended first-line management of asymptomatic brain metastases from EGFR mutant and ALK positive non-small cell lung cancer varies significantly according to specialty: an international survey of clinical practice
Bibliographic record
Abstract
Background: +) non-small cell lung cancer (NSCLC) is uncertain because of a lack of prospective evidence supporting tyrosine kinase inhibitor (TKI) monotherapy. Further understanding of practice heterogeneity is necessary to guide collaborative efforts in establishing guideline recommendations. Methods: We conducted an international survey among medical (MO), clinical (CO), and radiation oncologists (RO), as well as neurosurgeons (NS), of treatment recommendations for asymptomatic BrM (in non-eloquent regions) EGFRm or ALK+ NSCLC patients according to specific clinical scenarios. We grouped and compared treatment recommendations according to specialty. Responses were summarized using counts and percentages and analyzed using the Fisher exact test. Results: A total of 449 surveys were included in the final analysis: 48 CO, 85 MO, 60 NS, and 256 RO. MO and CO were significantly more likely than RO and NS to recommend first-line TKI monotherapy, regardless of the number and/or size of asymptomatic BrM (in non-eloquent regions). Radiotherapy in addition to TKI as first-line management was preferred by all specialties for patients with ≥4 BrM. NS recommended surgical resection more often than other specialties for BrM measuring >2 cm. Conclusions: Recommendations for the management of BrM from EGFRm or ALK+ NSCLC vary significantly according to oncology sub-specialties. Development of multidisciplinary guidelines and further research on establishing optimal treatment strategies is warranted.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".