Eligibility of real-world patients with metastatic breast cancer for clinical trials
Bibliographic record
Abstract
INTRODUCTION: The results of clinical trials in metastatic breast cancer (MBC) are generalized to real-world patients. This study determines the proportion of real-world patients who would be eligible for clinical trials and compares outcomes in eligible versus ineligible patients. METHODS: Patients diagnosed with MBC from 2004 to 2015 in a large Canadian province were included. Patients with one of the following criteria were considered ineligible: the presence of comorbid conditions (anemia, uncontrolled diabetes, heart disease, liver disease, and kidney disease) or a history of immunosuppression or prior malignancy. The likelihood of receiving cancer therapy was analysed using logistic regression models and factors affecting overall survival (OS) were assessed by Cox proportional hazards models. RESULTS: A total of 1585 patients with MBC were identified. The median age at diagnosis was 63 years. Of these, 512 (32.3%) patients were deemed ineligible in whom the two most common reasons for ineligibility were renal dysfunction (17.2%), and previous immunosuppression (7.8%). In the real world, ineligible patients were less likely to receive chemotherapy (29.5% vs 45.8%; P < 0.001) but not radiation treatment (7.6% vs 9.6%; P = 0.196) or hormonal therapy (57.6% vs 60.6%; P = 0.261). The 5-year OS of ineligible patients who received systemic therapy in the real-world was significantly better than those who did not. CONCLUSIONS: Despite being ineligible for clinical trials based on common eligibility criteria, many real-world patients receive systemic treatment and derive possible benefit. Broadening of inclusion criteria in clinical trials will enhance the representation of real-world patients and increase the generalizability of results.
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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".