Rationale, Strengths, and Limitations of Real-World Evidence in Oncology: A Canadian Review and Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Randomized controlled trials (RCTs) continue to be the basis for essential evidence regarding the efficacy of interventions such as cancer therapies. Limitations associated with RCT designs, including selective study populations, strict treatment regimens, and being time-limited, mean they do not provide complete information about an intervention's safety or the applicability of the trial's results to a wider range of patients seen in real-world clinical practice. For example, recent data from Alberta showed that almost 40% of patients in the province's cancer registry would be trial-ineligible per common exclusion criteria. Real-world evidence (RWE) offers an opportunity to complement the RCT evidence base with this kind of information about safety and about use in wider patient populations. It is also increasingly recognized for being able to provide information about an intervention's effectiveness and is considered by regulators as an important component of the evidence base in drug approvals. Here, we examine the limitations of RCTs in oncology research, review the different types of RWE available in this area, and discuss the strengths and limitations of RWE for complementing RCT oncology data.
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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 | 0.003 |
| 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 | 0.001 |
| 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 it