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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".