Optimizing the design of invasive placebo interventions in randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Placebo-controlled trials play an important role in the evaluation of healthcare interventions. However, they can be challenging to design and deliver for invasive interventions, including surgery. In-depth understanding of the component parts of the treatment intervention is needed to ascertain what should, and should not, be delivered as part of the placebo. Assessment of risk to patients and strategies to ensure that the placebo effectively mimics the treatment are also required. To date, no guidance exists for the design of invasive placebo interventions. This study aimed to develop a framework to optimize the design and delivery of invasive placebo interventions in RCTs. METHODS: A preliminary framework was developed using published literature to: expand the scope of an existing typology, which facilitates the deconstruction of invasive interventions; and identify placebo optimization strategies. The framework was refined after consultation with key stakeholders in surgical trials, consensus methodology and medical ethics. RESULTS: The resulting DITTO framework consists of five stages: deconstruct treatment intervention into constituent components and co-interventions; identify critical surgical element(s); take out the critical element(s); think risk, feasibility and role of placebo in the trial when considering remaining components; and optimize placebo to ensure effective blinding of patients and trial personnel. CONCLUSION: DITTO considers invasive placebo composition systematically, accounting for risk, feasibility and placebo optimization. Use of the framework can support the design of high-quality RCTs, which are needed to underpin delivery of healthcare interventions.
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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 | 0.648 | 0.640 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".