Palliative chemotherapy (CT) for advanced non-small cell lung cancer (NSCLC): Investigating disparities between patients who are treated versus those who are not.
Bibliographic record
Abstract
e17681 Background: Palliative CT in advanced NSCLC is associated with improved overall survival (OS) and quality of life, yet many patients remain untreated. In this study, we explored the differences between patients who did not receive palliative CT versus those who did, with a goal of better understanding and supporting the untreated. Methods: We performed a retrospective analysis of all newly diagnosed patients with advanced NSCLC seen at our institution between 2009 and 2012. Demographics, treatment, and survival data were collected. Fisher’s exact test assessed the association between CT use and baseline characteristics. Multivariate analysis of OS was performed using Cox regression models. Results: In total, 528 patients were seen: 291 (55%) received ≥ 1 line palliative CT, while 237 (45%) received none. Demographics were as follows: Median age 67, 55% male, 50% ECOG performance status (PS) 0-1, 48% with > 5% weight loss. Untreated patients were older (median 71 v 64, p < 0.01) and less fit (ECOG 0-1 in 27% v 69%, p < 0.01). More had weight loss (57% v 41%, p < 0.01), anemia (7% v 4%, p = 0.01), thrombocytosis (28% v 23%, p < 0.01), leukocytosis (38% v 32%, p < 0.01), and renal impairment (10% v 5%, p < 0.01). Reasons for no treatment included poor performance status (67%) and patient choice (23%). Median OS was shorter among untreated patients (3.9 v 10.7 months, HR 1.80 [95% CI 1.4-2.3], p < 0.01). In multivariate analysis, in addition to not receiving systemic therapy, factors associated with shorter OS were age, PS, weight loss, leukocytosis and thrombocytosis. Conclusions: Unsurprisingly, patients who did not receive CT had more poor prognostic features and worse OS. However, it is of concern that despite being seen in an active academic center, nearly half of all patients with advanced NSCLC received no anti-cancer treatment, most commonly due to poor PS. Current research primarily seeks to improve outcomes amongst those receiving systemic therapy, but this suggests that the lung cancer community must urgently advocate for the untreated. This should include more rapid diagnosis prior to functional decline, and development of therapies effective in a sicker population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".