Greater Expectations in a Cancer Trial: Absolute More Than Relative Survival Increases, Community More Than Academic Clinicians
Bibliographic record
Abstract
There is no consensus on how the difference between control and experimental outcome rates, the clinically important difference, should be estimated when designing a clinical trial. We sought to determine whether community and academic clinicians had different perceptions as to what would constitute a clinically important increase in survival, when asked to respond in absolute or relative terms, before a trial was started rather than when the results were already known. A telephone survey of 25 practicing Canadian oncologists was performed. Questions were asked as to the importance of acceptable and minimally acceptable improvements in survival for a hypothetical trial of pancreatic cancer where the baseline survival was expected to be between 2 and 8 months. Responses were sought for absolute (additional months) or relative gains (percent improvement) in survival. The mean absolute additional survival expectations corresponded to at least a doubling of baseline survival and tended to be greatest when the prognosis was poorest (p = 0.06). Relative expectations for improved survival varied with baseline survival (p < 0.001). When improvement in survival was requested in relative terms, the median expected improvement was 25%. This is highly significantly different than when survival improvements were requested in absolute terms (p < 0.0001). Median absolute survival expectations were greater for community as compared with academically affiliated physicians (p = 0.046). We found that physicians are inconsistent in their interpretation of qualitative data. What constitutes a potentially clinically important treatment effect differs whether viewed in relative or absolute terms before the performance of a trial. Expectations were greatest when the prognosis was poorest and differed between community and academic physicians.
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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 | 0.095 | 0.271 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".