Serum Carcinoembryonic Antigen Level Predicts Cancer-Specific Outcomes of Resected Non-Small Cell Lung Cancer With Interstitial Pneumonia
Bibliographic record
Abstract
BACKGROUND: It has been well accepted that the prognosis of non-small cell lung cancer (NSCLC) patients with interstitial pneumonia (IP) is significantly poor. However, there are only a few studies that indicated the prognostic factors, especially tumor markers, among NSCLC patients with IP. METHODS: Forty-one NSCLC patients with IP who underwent surgery at our institution were included. Patients died of other diseases including postoperative acute exacerbation (AE) of IP were excluded. Univariate and multivariate analyses were calculated by the Cox proportional hazards regression model. RESULTS: The 5-year cancer-specific survival of overall and stage I patients were 37.4% and 39.2%, respectively. The 5-year cancer-specific survival of patients with high serum carcinoembryonic antigen (CEA) level was 9.4%, while that with normal serum CEA level was 55.6%. However, serum cytokeratin-19 fragment (CYFRA 21-1) and squamous cell carcinoma-related antigen (SCC) levels were not associated with patients' survival. Furthermore, serum CEA level was significantly associated with poorer cancer-specific survival in univariate and multivariate analyses. CONCLUSIONS: This study demonstrated that serum CEA level might serve as an efficient prognostic indicator after surgery in NSCLC with IP.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".