Equipoise and the Ethics of the Canadian Lung Volume Reduction Surgery Trial study: Should There Be a Randomized, Controlled Trial to Evaluate Lung Volume Reduction Surgery?
Bibliographic record
Abstract
The physical improvement is so great following lung volume reduction surgery that there is growing opinion that a randomized, controlled trial is unnecessary. A randomized, controlled trial, it is argued, would deprive those patients randomly assigned to the nonsurgical treatment arm the 'benefit' of lung volume reduction surgery. Entering a trial in which one arm leads to a surgical intervention and the other to best medical management also poses a variety of ethical difficulties. If one is to be offered surgery, there must be perceived benefit because the physician has an obligation to offer the best possible treatment for his or her patient. If a patient agrees to have surgery, the expectation is that surgery would help. Thus, a patient randomly assigned to the medical arm of a trial may easily believe that he or she is being deprived of surgery that may help them. This paper illustrates this dilemma using the Canadian Lung Volume Reduction Surgery Trial. The authors discuss the concept of 'equipoise' in three dimensions, adding community equipoise to theoretical equipoise and clinical equipoise earlier described by Freedman. The paper concludes that the Canadian Lung Volume Reduction Surgery Trial needs to continue because of the clinical equipoise that drives it.
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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 | 0.481 | 0.589 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.017 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".