Eligibility of real-world patients with metastatic lung cancer for clinical trial participation: A population-based analysis.
Bibliographic record
Abstract
93 Background: Due to highly selective enrollment in clinical trials, the generalizability of results may be limited. This study aimed to identify the proportion of real-world patients with metastatic lung cancer (MLC) eligible to participate in a clinical trial. Methods: We identified patients diagnosed with MLC in a large Canadian province from 2004 to 2017. Ineligibility to participate in a clinical trial was defined by common exclusion criteria: age > 75 years, anemia, comorbid conditions (heart disease, uncontrolled diabetes, kidney disease, or liver disease) and history of a prior malignancy or immunosuppression. Logistic regression models were used to describe the likelihood of receiving systemic therapy and Cox regression models were constructed to determine the association of trial ineligibility with overall survival (OS). Results: A total of 13,996 patients were included; the median age was 70 years and 46.9% were women. Of these, 8,615 (61.6%) were trial-ineligible. The common reasons for ineligibility were age > 75 years (11.5%), abnormal renal function (8.3%) and prior immunosuppression (3.2%). Further, 32.3% of patients were ineligible by multiple exclusion criteria. In the real-world, 40.6% and 21.8% of trial-eligible and ineligible patients received systemic therapy (P < .001), respectively. After adjusting for age and sex, trial-ineligible patients had lower odds of receiving systemic therapy (odds ratio, .84; 95% confidence interval [CI], .76-.92; P < .001). At a median follow-up of 66.2 months, the median OS of trial-eligible patients was 5.1 months as compared to 2.9 months in those deemed ineligible (P < .001). Receipt of systemic therapy was associated with longer OS in both trial-eligible (10.5 vs 2.7 months, P < .001) and ineligible (9.3 vs 2.1 months, P < .001) patients. In a Cox regression model that adjusted for age, sex and systemic therapy, ineligibility was predictive of worse OS. Conclusions: More than half of patients with MLC are ineligible to participate in clinical trials. Real-world use of systemic therapy was generally low, but its use was associated with improvement in OS even among individuals considered trial-ineligible. Clinical trials should broaden their eligibility criteria to better represent the phenotype of real-world patients so that findings are more generalizable. [Table: see text]
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".