Prognostic significance of lymph nodes assessment during pulmonary metastasectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Lung metastasectomy is an accepted treatment modality worldwide. Whether the addition of lymph node dissection to the procedure is useful remains, however, unknown. Methods: We performed a systematic review of the literature analyzing MEDLINE, Embase, until 31st October 2021. We included all studies which met the inclusion criteria aiming to determine if the addition of lymph node tissue dissection/sampling to lung metastasectomy offers survival benefits when compared to patients who do receive lymph node tissue dissection. Secondary outcomes were 3- and 5-year overall survival (OS) and disease-free survival (DFS). Each study was assessed for risk of bias. The data collected from the included studies were pooled using reconstruction of individual-level patient data and pooling of reported 5-year odds ratios (ORs). Interstudy heterogeneity was estimated with visual inspection of forest plots and calculation of the I2 inconsistency statistic. Results: We found 11 eligible studies that included a total of 3,310 patients. The most common primary tumor type was colorectal cancer (1,740 patients) and the most commonly performed operative procedure was wedge resection (57%) followed by lobectomy (39%). When resection status was reported, R0 resection was achieved in 90% of the cases. Nine studies did not show a statistically significant effect of lymph nodes dissection or sampling on the 5-year OS with a pooled hazard ratio (HR) of 0.94 [95% confidence interval (CI): 0.82, 1.08; I2=26%; 95% prediction interval (PI): 0.70, 1.24]. Regarding DFS, the pooled HR 0.60 (95% CI: 0.44, 0.80; I2=31%; 95% PI: 0.12, 2.09). Conclusions: The addition of lymph node tissue dissection during lung metastasectomy was not associated with a significant benefit in OS and showed a slight tendency towards a better DFS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".