Local tumor control and survival outcomes of percutaneous radiofrequency ablation plus post-ablation chemotherapy for lung tumors in nonsurgical patients: A meta-analysis.
Bibliographic record
Abstract
9550 Background: Local recurrence is a frequent outcome post radiofrequency ablation (RFA) for local control of lung tumors. We sought to examine local tumor control and survival benefits of RFA plus post-ablation chemotherapy versus RFA alone for management of lung tumors in non-surgical patients. Methods: Search strategy: MEDLINE, the Cochrane Library, and EMBASE databases from January 2000 to December 2012. Inclusion criteria: RFA +/- post-ablation chemotherapy in non-surgical patients with solid lung tumors. Exclusion criteria: Post-RFA radiation therapy, biologics, brachytherapy, or other ablation modalities. Outcomes: Local tumor progression (LTP), overall survival (OS), and disease-free survival (DFS) at 12 month follow-up. Statistical analysis: Fixed effect analyses, bias assessment, and sensitivity analyses (BioStat Inc., NJ, USA). Results: RFA plus post-ablation chemotherapy group: 11 clinical studies, 684 patients (mean age 64 years [range 50 to 74]; 434 men, ECOG ≤2), ablation of 1,314 lung tumors, with a 4:1 ratio being <3cm versus ≥3cm in diameter, and a 1:4 ratio being primary versus metastatic. RFA alone group: 38 clinical studies, 1,874 patients (mean age 65 years [range 49 to 75]; 1,041 men, ECOG ≤2), ablation of 2,604 lung tumors, with a 2.1:1 ratio being <3cm versus ≥3cm in diameter, and a 1:1 ratio being primary versus metastatic. RFA plus post-ablation chemotherapy versus RFA alone: LTP of 15% over median follow-up of 31 months [range 12 to 59]) versus 19% over median follow-up of 21 months [range 12 to 29]); OR 0.73 (95% CI: 0.61-0.86, p<0.05) at 12 month follow-up. OS was 89% versus 78%, respectively, at 12 month follow-up; OR 1.52 (95% CI: 1.16-2.00, p=0.003). DFS was 90% versus 82%, respectively, at 12 month follow-up; OR 3.18 (95% CI: 2.04-4.96, p<0.05). Sensitivity analyses were robust, publication bias relatively narrow, and heterogeneity within acceptable limits; Q statistic<21; p>0.13 for all outcomes. Conclusions: This meta-analysis reveals that RFA plus post-ablation chemotherapy of lung tumors yields improved outcomes in terms of LTP, OS, and DFS compared with RFA alone.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".