Patterns of Relapse in Small Cell Lung Cancer: Competing Risks of Thoracic versus CNS Relapse
Bibliographic record
Abstract
INTRODUCTION: Treatment algorithms for small cell lung cancer (SCLC) are determined largely by the Veterans Affairs Lung Cancer Staging Group (VALCSG) staging (limited (LS) versus extensive (ES) stage). Relapse occurs frequently; however, patterns of relapse, in particular the competing risk of thoracic and central nervous system relapse, are not well described. This study describes patterns of relapse in SCLC patients treated at a large tertiary institution in Ontario, Canada. MATERIALS AND METHODS: A retrospective cohort of SCLC patients treated at the Juravinski Cancer Centre was reviewed. Data were abstracted from the medical record on demographic, disease, treatment and outcome variables. The primary outcome was a description of the patterns of relapse stratified by disease stage. Multivariate analysis was performed to identify prognostic variables for thoracic and CNS relapse. RESULTS: Two hundred and twenty nine patients were treated during the study period (LS-83, ES-146). Relapse occurred in the majority of patients (isolated thoracic-28%, isolated CNS-9%, extrathoracic-9%, thoracic/extrathoracic-14%, systemic and CNS-13%). The median OS was consistent with published data (LS-21.8 months, ES-8.9 months). ES disease and elevated LDH were prognostic for increased thoracic relapse, whereas poor PS and older age were prognostic for lower central nervous system (CNS) relapse. DISCUSSION: Thoracic relapse and CNS relapse represent competing risks for patients with SCLC. Decisions about incorporating thoracic or CNS radiation are complex. More research is needed to incorporate performance status and LDH into treatment algorithms.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".