Treatment patterns and clinical outcomes in lung cancer patients with brain metastasis
Bibliographic record
Abstract
18010 Background: Controversies still exist in the optimal management of patients with meta- static brain cancer. Lung cancer is the most common cause of metastatic brain cancer and the present study examines the treatment pattern and clinical outcome of this group of patients. Methods: Using the provincial cancer registry, all patients in Manitoba, Canada, with a history of lung cancer diagnosed with brain metastases during 2003 and 2004 were identified and a detailed chart review was carried out. Univariate and multivariate logistic analysis were used to identify significant variables affecting 1-year survival. Results: A total of 229 patients were identified of which 112 were male. The median age was 65 (range = 39–90). Of these patients, 69% had non-small cell lung cancer, 17% had small cell cancer, and 14% did not have the histological diagnosis available. A single brain metastasis, 2–4 brain metastases, and >4 brain metastases was identified in 25%, 25%, and 44% of the patients, respectively. The treatments for brain metastases were surgery (8%), gamma-knife radiosurgery (GKS) (7%), whole brain radiotherapy (WBRT) (62%), and no treatment (22%). Median survival for all patients was 98 days. On univariate analysis, 1-year survival was significantly influenced by surgery/GKS, age < 65, and controlled systemic disease (Odds ratio = 7.90; 5.43; 3.72 respectively, P < 0.02 for all) but not by sex, number of brain metastases or time from initial diagnosis to brain metastasis. On multivariate analysis, surgery/GKS and age remained significant (Odds ratio = 5.56, and 4.80, P < 0.01). Median survival (days) by treatment type were: surgery/GKS = 297(178–461), WBRT = 105(82–119), none = 39(29–50); log rank <0.001. Conclusions: In our patient population, with metastatic brain cancer arising from the lung, aggressive focal therapy (surgery or GKS) in younger patients (< 65) was associated with significantly better overall survival. No significant financial relationships to disclose.
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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 | 0.001 | 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) | 0.002 | 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".