Impact of Timeliness of Surgical Treatment on the Outcomes of Patients with Non‐metastatic Non‐small Cell Lung Cancer: Findings From the PLCO Trial
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess the impact of timeliness of surgical resection among patients with non-metastatic non-small cell lung cancer (NSCLC) treated with upfront surgery. METHODS: Cases with confirmed non-metastatic NSCLC diagnosis treated with upfront surgery within the cohort of participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial were included in the current study. Multivariate logistic regression analysis was used to assess factors predicting time from diagnosis to surgical resection. Multivariate Cox regression analysis was used to assess factors affecting lung cancer-specific survival. RESULTS: A total of 1022 patients were included in the current analysis. A total of 873 patients underwent surgical resection within 30 days of diagnosis, while a total of 149 patients underwent surgical resection after 30 days from diagnosis. Through multivariate logistic regression analysis, the following factors were predictive for longer time to surgical resection: older age (odds ratio 1.077; 1.043-1.112; P < 0.001) and advanced stage at presentation (odds ratio 1.923; 1.056-3.502; P = 0.033). Through multivariate Cox regression analysis, time to surgical resection (≤30 days vs. >30 days) did not affect lung cancer-specific survival (hazard ratio 0.999; 0.739-1.350; P = 0.994). When the same multivariate analysis was repeated using time to surgical resection as a continuous variable, there was no impact on lung cancer-specific survival (hazard ratio 1.002; 0.997-1.007; P = 0.383). CONCLUSIONS: Time to surgical resection did not affect survival outcomes of non-metastatic NSCLC patients. Current therapy timeline targets need to be reviewed in our healthcare systems in order to redirect and prioritize the existing resources.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".