Readiness of Healthcare Systems to Generate Real-World Evidence: Reliability of CT Radiographic End Points for Evaluation of First-Line Systemic Treatment
Bibliographic record
Abstract
PURPOSE: Regulatory agencies such as the US Food and Drug Administration and health technology assessment bodies are increasingly using real-world evidence (RWE). The ability of healthcare systems to reliably generate response rate and progression-free survival from real-world data is unknown. We examined the capacity of a single-payer system to provide RWE by evaluating the frequency of computed tomography (CT) imaging during standard first-line metastatic systemic treatment of breast, colorectal, and lung cancer. METHODS: A 1-year cohort of patients with metastatic-at-diagnosis breast, colorectal, and lung cancer treated with first-line systemic therapy (excluding hormone therapy) referred to BC Cancer in 2016 was retrospectively reviewed for first-line treatment and CT imaging. Duration of first-line treatment was calculated from the first to the last dose of therapy. CT imaging was counted from the start of therapy to 8 weeks after the last therapy dose. RESULTS: A cohort of 664 patients was identified from the BC Cancer Registry. Distribution of metastatic disease at diagnosis was breast (n = 82), colorectal (n = 214), and lung (n = 368) cancer. For breast, colorectal, and lung cancer, there was a baseline CT within 4 weeks of treatment initiation in 59%, 51%, and 48% of patients, with median duration of first-line treatment of 14.6, 25.3, and 11.9 weeks and median CT imaging interval of 9.1, 9.0, and 6.1 weeks. CONCLUSION: In our publicly funded healthcare system, availability of baseline CT imaging was 48% to 59% and the frequency of assessment ranged from 6.1 to 9.1 weeks, subject to patterns of practice and resource availability. Our system was not capable of providing RWE for image-based end points. Alternative end points should be considered to capitalize on the wealth of real-world data.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".