Real World Patient Eligibility for Second Line Lurbinectedin Based Treatment in Small Cell Lung Cancer: Understanding Epidemiology and Estimating Health Care Utilization
Bibliographic record
Abstract
Background: In the ATLANTIS study, second-line lurbinectedin/doxorubicin did not improve overall survival (OS), however patients with a chemotherapy-free interval (CTFI) of ≥180 days had an improved progression free survival (PFS). The objective of this retrospective study was to identify the proportion of real-world small cell lung cancer (SCLC) patients who are suitable for lurbinectedin-based therapy based on these criteria. Methods: A retrospective study of all SCLC referred to BC Cancer between 2012 and 2017 was conducted. Patient demographics, staging, treatment, and survival data were collected retrospectively. Baseline characteristics were compared using descriptive statistics. OS was calculated using Kaplan–Meier curves. Statistically significant p-value was <0.05. Results: A total of 1048 patients were identified. Baseline characteristics: median age 68 years, 47% male, 61% current smoking status, 68% extensive disease. Best supportive care was received by 22%. First-line systemic therapy was platinum doublet for 71% of the population. Second-line systemic therapy was delivered to 22%. Of the 219 patients who received second-line systemic therapy after platinum doublet, 183 patients had a CTFI of ≥90 days and 107 patients had a CTFI of ≥180 days. Patients originally treated as limited stage disease, received platinum doublet as second line, received thoracic radiation (RT) or prophylactic cranial irradiation (PCI) were more likely to have a CTFI of ≥90 and ≥180 days. Conclusion: In our real-world SCLC population, only 21% of the SCLC population received second-line therapy after platinum doublet with 17% achieving CTFI of ≥90 days and 10% CTFI of ≥180 days. Based on this retrospective review, only a small fraction of platinum-treated patients would be preferentially offered lurbinectedin in the second-line setting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".