Prognosis of thoracic radiation-induced metastatic NSCLC.
Bibliographic record
Abstract
e21095 Background: Radiation is known to be the main risk factor for secondary malignancies. However, metastatic lung cancer prognosis when thoracic radiation was previously given is unknown. Methods: The OACIS and SARDO patient databases were used to identify 1365 patients who received treatment at the University Health Center in Montreal (CHUM) hospital for metastatic NSCLC between 2006 and 2014. To control for genetic susceptibility, we divided our population in three groups. Group A: patients who received previous thoracic radiation, group B: patients with previous cancer but no thoracic radiation, group C: metastatic lung cancer with no history of cancer or thoracic radiation. Results: In group A, the most frequent cancer that received thoracic radiation previously was breast cancer (42%). In group B, the most frequent cancer treated without thoracic radiation was genito-urinary tract cancers (prostate cancer 43%). Group A had the best mOS 8 months compared to group B 6 months and to group C 4 months (P = 0.005). In group A only, women had significantly longer mOS (12 months) than men (5 months) (P = 0.026), but not in groups B and C. Only. 65% of patients of our population received systemic treatment with no difference between groups. Amongst treated patients, the mOS was significantly higher in group A (14 months) compared to group B (9 months) and group C (7 months) (P = 0.015). There was no significant PFS difference between the groups (8 months vs. 6 months vs. 6 months; P = 0.05). Conclusions: Surprisingly, previous thoracic radiation before metastatic lung cancer had a better prognosis than patients with genetic susceptibility to cancer or the control arm with no previous cancer that developed metastatic lung cancer. Radiation-related lung cancer should not be an obstacle to treat these population with aggressive treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".