Treatment of Lung Cancer in Medically Compromised Patients
Bibliographic record
Abstract
Outcomes for patients with lung cancer have been improved substantially through the integration of surgery, radiation, and systemic therapy for patients with early-stage disease. Meanwhile, advances in our understanding of molecular mechanisms have substantially advanced our treatment of patients with advanced lung cancer through the introduction of targeted therapies, immune approaches, improvements in chemotherapy, and better supportive care. However, the majority of these advances have occurred among patients with good functional status, normal organ function, and with the social and economic support systems to be able to benefit most from these treatments. The aim of this article is to bring greater attention to management of lung cancer in patients who are medically compromised, which remains a major barrier to care delivery. Impaired performance status is associated with poor outcomes and correlates with the high prevalence of cachexia among patients with advanced lung cancer. CT imaging is emerging as a research tool to quantify muscle loss in patients with cancer, and new therapeutics are on the horizon that may provide important adjunctive therapy in the future. The benefits of cancer therapy for patients with organ failure are poorly understood because of their exclusion from clinical trials. The availability of targeted therapy and immunotherapy may provide alternatives that may be easier to deliver in this population, but clinical trials of these new agents in this population are vital. Patients with lower socioeconomic status are disproportionately affected by lung cancer because of higher rates of tobacco addiction and the impact of socioeconomic status on delay in diagnosis, treatment, and outcomes. For all patients who are medically compromised with lung cancer, multidisciplinary approaches are particularly needed to evaluate these patients and to incorporate rapidly changing therapeutics to improve outcomes.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".