True Prevalence of Unforeseen N2 Disease in NSCLC: A Systematic Review + Meta-Analysis
Bibliographic record
Abstract
Patients with unforeseen N2 (uN2) disease are traditionally considered to have an unfavorable prognosis. As preoperative and intraoperative mediastinal staging improved over time, the prevalence of uN2 changed. In this review, the current evidence on uN2 disease and its prevalence will be evaluated. A systematic literature search was performed to identify all studies or completed, published trials that included uN2 disease until 6 April 2023, without language restrictions. The Newcastle-Ottawa Scale (NOS) was used to score the included papers. A total of 512 articles were initially identified, of which a total of 22 studies met the predefined inclusion criteria. Despite adequate mediastinal staging, the pooled prevalence of true unforeseen pN2 (9387 patients) was 7.97% (95% CI 6.67-9.27%), with a pooled OS after five years (892 patients) of 44% (95% CI 31-58%). Substantial heterogeneity regarding the characteristics of uN2 disease limited our meta-analysis considerably. However, it seems patients with uN2 disease represent a subcategory with a similar prognosis to stage IIb if complete surgical resection can be achieved, and the contribution of adjuvant therapy is to be further explored.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".