Clinic image surveillance reduces mortality in patients with primary hepato-gastrointestinal cancer who develop second primary lung cancer
Bibliographic record
Abstract
Second primary cancer is prevalent in patients with gastrointestinal (GI) cancer, for which lung cancer is the most common and associated with high lethality. Image screening for lung cancer was proved to be effective in early diagnosis and lower mortality. However, trials of screen for lung cancer generally excluded patients with a previous diagnosis of malignancy. The study aimed to investigate the outcome of second primary lung cancer and the factor that improve survival in patients with hepato-GI cancer.A total of 276 patients with secondary lung cancer were found among 3723 newly-diagnosed lung cancer patients diagnosed in Chang Gung Memorial Hospital, between 2010 and 2014. Patients' clinical characteristics, stages and survival were recorded and analyzed. The patients were separated into 2 groups: Group I was defined as lung cancer detected in original primary cancer clinic and group II patients defined as lung cancer detected in other medical places.Sixty-nine cases with primary GI-hepatic and secondary lung cancer were diagnosed (42 (60.8%) in Group I and 27 (39.1%) in Group II). Although both groups had comparable primary cancer stages and treatment, more patients in Group I than Group II were diagnosed as early stage lung cancer (stage I-II: 40.5% vs 11.1%; P = .023). Group II had larger lung tumor sizes than Group I (4.7 vs 3.5 cm; P = .025). Group I showed better 5-year overall survival than Group II (P = .014, median survival: 27 vs 10 months). Among Group II, only 37% had received image follow up in clinic compared with 67% of Group I cases (P = .025). Patients with chest image follow up in clinics also had better 5-year overall survival (P = .043).GI-hepatic cancer was the most common primary malignancy in the lung cancer cohort. Patients had better survival outcome when secondary lung cancer was diagnosed in original primary cancer clinic. Chest image screening strategy may contribute better survival in secondary lung cancer due to detection at an earlier stage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".