The role of palliative care in the lung cancer patient: can we improve quality while limiting futile care?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lung cancer is the leading cause of cancer death worldwide, and is diagnosed in the advanced stage in 70% of patients. This study will summarize the most up-to-date strategies in supportive care for patients with metastatic lung cancer. RECENT FINDINGS: Two recent systematic reviews concluded that longer-course radiation treatment offers a survival and symptom benefit over short-course treatment in patients with good performance status. Whereas several meta-analyses have demonstrated the benefit of palliative chemotherapy for quality of life and survival, the optimal drug combination and number of courses remain under study, and proper stratification of patients is essential. Clinical practice guidelines are available for evidence-based management of symptoms common in patients with lung cancer. Early integration of specialized palliative care shows promise as a means of improving patient care and limiting unnecessary treatment. SUMMARY: Supportive care for the patient with advanced lung cancer should involve consideration and discussion of all therapeutic options that could provide benefit. Depending on the clinical situation, these could include chemotherapy or radiation, and should always include appropriate symptom management and family support. Research incorporating symptom and quality-of-life measures is challenging, but is also essential to inform excellent supportive care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".