Treatment Approaches in 102 Elderly Patients With Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: The life expectancy and presence of co-morbidities cause reservations in treatment decisions for elderly patients with cancer. In this study, we retrospectively evaluated 102 patients who are considered as middle-old aged (aged 75 - 84) by gerontologists. METHODS: Medical records of patients were reviewed. One hundred and two patients with a diagnosis of non-small cell lung cancer (NSCLC) whose follow-up ended with death between March 2006 and May 2013 were examined. RESULTS: The median age at diagnosis was 77 (75 - 85) years. Thirty-three patients (67.6%) were over 80 years old. The number of patients with metastasis was 57 (55.8%). Forty-two (41.2%) patients had stage IIIA and IIIB disease. Fifteen of the metastatic patients (26.3%) were given chemotherapy, while 12 of the non-metastatic patients (26.6%) were given chemotherapy. Of the non-metastatic patients, 25 (55.6%) were treated with radiotherapy, and five (11.1%) were treated with chemotherapy. The median duration of follow-up was 4 (1-55) months. Progression-free survival (PFS) was 4 months in non-metastatic patients, and 3 months in metastatic patients. Overall survival (OS) was 4 months. OS rates for 1 and 2 years were 10% and 2%. CONCLUSION: Chemotherapy and radiotherapy may be administered even to patients of this age group. The beneficial effect of chemotherapy in patients with metastasis on OS is an important finding of our study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".