Additional file 1 of A comparison of the efficacy of antiangiogenic agents combined with chemotherapy for the treatment of non-small cell lung cancer: a network meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. Rank probabilities of each treatment for different outcome measures based on the network meta-analysis. Table S2. Begg's and Egger's tests of the single-arm meta-analysis. Table S3. PRISMA checklist of the current network meta-analysis. Figure S1. The details of quality assessment of included studies. A. The details of quality assessment using the Cochrane Collaboration's risk of bias tool for 25 randomized controlled trials. B. The details of quality assessment using Newcastle Ottawa Scale for 4 nonrandomized controlled trials. Figure S2. Results of single-arm meta-analysis of four common adverse events of bevacizumab combined with chemotherapy and Endostar combined with hemotherapy. A. Anemia; B. Leukopenia. C. Thrombocytopenia. D. Vomiting. Figure S3. Funnel plots of the single-arm meta-analyisis. A: ORR. B: OS. C: PFS. D: HR of OS. E: HR of PFS. F: Anemia. G: Leukopenia. H: Thrombocytopenia. I: Vomiting.
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 | 0.006 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.809 | 0.037 |
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".