Supplementary Table 1: Impact of ALK fusion variant on clinical outcomes in EML4-ALK NSCLC patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Supplementary Table 1: Impact of ALK Fusion Variant on Clinical Outcomes in EML4-ALK NSCLC Patients: a systematic review and meta-analysis.Quality assessment of eligible studies using the Newcastle Ottawa quality assessment scale AbstractBackground: Emerging studies showed that ALK-fusion variants were associated with heterogeneous clinical outcomes. However, contradicting conclusions drew in some other studies considered no correlation between ALK variants and prognoses. Methods: we performed a systematic review and meta-analysis to evaluated the prognostic value of EML4-ALK fusion variants for the outcome of patients. Results: 28 studies were included in our analysis. According to the pooled results, patients harboring variant 1 showed equivalent PFS and OS with non-v1 (HR for PFS: 0.91(0.68-1.21), p=0.499; for OS: 1.12(0.73-1.72), p=0.610). Similarly, patients with v3 showed the same disease progress with non-v3 (pooled HR for PFS=1.07(0.72-1.58), p=0.741). However, pooled results for OS suggested that patients with v3 had a worse survival than non-v3 (HR=3.44(1.42-8.35), p=0.006). Conclusion: Overall results suggested that patients with v1 exhibited no significant difference with non-v1 in terms of OS and PFS, while v3 was associated with shorter OS in ALK-positive NSCLC patients.
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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 | 0.005 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.234 | 0.006 |
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".