The effect of prior cancer on non‐small cell lung cancer trial eligibility
Bibliographic record
Abstract
OBJECTIVES: Approximately 20% of patients diagnosed with non-small cell lung cancer (NSCLC) have a history of prior (non-lung) cancer. Patients with prior cancer are frequently excluded from clinical trials. We aimed to assess the potential impact of prior cancer on commonly used clinical trial endpoints. MATERIALS AND METHODS: Clinical trials of systemic therapy for incurable NSCLC from clinicaltrials.gov were reviewed to determine the frequency of exclusion on the basis of prior cancer. A cohort of patients with incurable NSCLC and prior cancer, treated with first-line systemic treatment at our institution were reviewed as a surrogate clinical trial population. A list of priori events was developed to capture the potential for prior cancer to negatively affect clinical trial conduct or endpoints. The proportions of patients that developed an outcome were assessed. RESULTS: Among trials registered on clinicaltrials.gov, 66% listed prior cancer in the eligibility criteria, and of these 35% excluded patients with prior cancer in the last 5 years. Of NSCLC patients treated with systemic therapy at Princess Margaret Cancer Center, 20% had prior cancer, of these, breast (20%) and prostate (19%) were the most common malignancies. Median time between prior cancer and NSCLC was 82 months. Median survival was 20 months. For patients without evidence of active prior cancer at baseline, and not on active therapy for prior cancer, no patients had evidence of a recurrence of prior cancer during the treatment and follow-up for the NSCLC, nor died from prior cancer. However, two patients developed new primaries. CONCLUSIONS: A history of prior cancer has a low likelihood of impacting clinical trial endpoints in patients with incurable NSCLC, if not active or requiring treatment. These findings should be validated in larger data sets.
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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 | 0.479 | 0.525 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".