Is the Use of Placebo Controls Ethically Permissible in Clinical Trials of Agents Intended to Reduce Fractures in Osteoporosis?
Bibliographic record
Abstract
Substantial progress has been made in developing treatments that reduce the risk of fractures in osteoporosis. However, available treatments are only partially effective, they are not widely used, and there is need to search for more effective means of fracture prevention. Currently known effective means of reducing fractures were found using randomized placebo-controlled trials. The use of placebo controls in clinical trials has been a subject of significant controversy in recent years. The Declaration of Helsinki revision of October 2000 caused great concern among clinical investigators about the future use of placebo controls if known effective therapeutic agents are available. A working group of ethicists, clinical trial design experts, and clinical investigators examined the current state of knowledge of osteoporosis treatment and trials. They concluded that if placebo controls put subjects at substantial risk of serious outcomes, they are not ethically permissible. Placebo controls in osteoporosis trials with fracture as the measured outcome are permissible only under narrowly defined conditions. Placebo controls may be used if competent, well-informed patients refuse approved therapies for sound reasons, there is a reasonable basis for substantial disagreement or lack of consensus among professionals about whether approved treatments are better than placebos, or subjects are refractory to known effective agents. Active control trials are permissible and desirable if they can be designed and conducted in ways that overcome the interpretive difficulties often associated with such trials.
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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 | 0.737 | 0.826 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.041 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".