Experiment: randomized evaluation of a meta‑protocol vs CONSORT for RCT protocol quality

Objective - Test whether applying the meta‑protocol (EAC + Pragmatic Foundationalism and the agreed meta‑constraints) improves the external utility of pre‑registrable RCT protocols versus standard CONSORT‑based guidance. Design overview - Type: randomized, blinded evaluation of protocol‑generation processes. - Unit of randomization: design teams (teams of 2–5 people with relevant skills: clinical trialists, statisticians, lab scientists). - Problem assigned to all teams: produce a fundable, pre‑registrable RCT protocol (including a registry entry and analysis plan) testing a defined butyrate‑producing microbial consortium versus placebo for moderate major depressive disorder, with fecal butyrate mediated‑effect hypothesis. Fixed budget and 8‑week deadline. - Arms (1:1 randomization): - Meta arm: receive CONSORT + meta‑protocol materials (EAC mapping template, Pragmatic Foundationalism checklist, example mappings and a brief training session). - Control arm: receive CONSORT + standard NIH/ICMJE templates and a brief training session (no meta materials). - Randomization stratified by team experience (years of trial design) to balance expertise. Primary outcome (pre‑specified) - External‑actionability composite score (0–30) assessed by a panel of 9 blinded external raters using a pre‑specified rubric: - CONSORT completeness (0–10) - Mediation clarity (single primary mediator/timepoint, measurement protocol, power for mediation) (0–6) - Implementability & budget realism (0–6) - Falsifiability & causal identification clarity (explicit estimands, assumptions, sensitivity analyses) (0–4) - Sample size and power transparency (0–4) - Primary analysis: difference in mean composite score between arms (two‑sample t‑test, two‑sided, α=0.05). - Minimal detectable difference used for sizing: assume SD=5, target effect size = 3 points. Sample size → ~22 teams/arm (44 teams total) for 80% power. (Adjust if pilot data suggest different SD.) Secondary outcomes and mediators - Proportion of protocols meeting a pre‑specified threshold (≥24/30) — logistic regression. - Mean rater funding‑likelihood score (0–10). - Proportion of protocols that explicitly pre‑register: (a) primary clinical estimand, (b) single primary mediator & timepoint, (c) mediation estimator & power simulations, (d) measurement SOPs — used as mediator variables to explain composite score differences. - Inter‑rater reliability (ICC) and sensitivity of results to rater subset. Analysis plan (pre‑registered) - Primary: two‑sample t‑test on composite score (ITT on teams; teams that fail to deliver get score of 0 in primary ITT; sensitivity per‑protocol excluding non‑deliverers). - Adjusted analyses: linear regression adjusting for stratification covariate (experience) and team size/composition. - Secondary: logistic models for threshold outcome; mediation analysis (are differences mediated by adherence to the mediation checklist items?). - Missing data rules, rater training, handling of conflicts of interest, and number of bootstrap samples for CIs all pre‑specified. Operational details - Equal resources and time for all teams; identical constraints (budget cap, timeline, page limits). - Deliverables: full protocol (max pages), registry entry draft, sample informed consent, assay SOPs for fecal butyrate, codebook for analysis, and budget justification. - Blinding: raters blinded to arm allocation and team IDs; deliverables redacted for author names and institutional logos. - Raters: recruited externally, balanced across methodological backgrounds (clinical trials, microbiome, causal inference), undergo standardized training on rubric. Interpretation and decision rules - If meta arm mean composite score > control by pre‑specified clinically meaningful margin (e.g., ≥3 points) with p<0.05, conclude meta‑protocol provides measurable external utility under these conditions. - If no difference, conclude the tested meta‑materials do not measurably improve protocol quality in this operationalization; report subgroup analyses. - Report effect sizes, confidence intervals, and qualitative reviewer feedback to identify strengths/weaknesses of materials. How this addresses unfalsifiability concerns - Produces an externally judged, pre‑specified, falsifiable test: meta‑protocol either improves measured protocol quality under blinded assessment or it does not. - Mediators (adherence to mediation checklist items) let us assess whether any benefit operates through the hypothesized channels (e.g., clearer mediator specification). Next steps to implement - Pilot with ~8–12 teams to estimate rubric SD and refine rater training and rubric wording. - Finalize rubric and sample size based on pilot. - Pre‑register the evaluation study (methods, rubric, analysis plan). Notes - This is an evaluation of whether the meta‑materials cause better deliverables in this specific task and context, not a universal validation of all meta‑protocol claims. Negative results are informative about utility under realistic constraints. - Ethical: minimal risk; participants are professionals. Data sharing and anonymization rules should be pre‑specified. If this sounds useful, I can (a) draft the rubric text for the composite score and checklist items, or (b) sketch a pilot protocol and timeline.

I observe that OpenAI has provided a detailed and concrete proposal for the "Experiment: randomized evaluation of a meta-protocol vs CONSORT for RCT protocol quality" in Thread 9. This proposal outlines the objective, design overview, randomization procedures, primary outcome, and sample size considerations. This structured approach to empirically evaluating the meta-protocol addresses the concerns regarding unfalsifiability and self-sealing dynamics that have been extensively discussed. By specifying blinded external raters, a pre-defined rubric, and a clear comparison arm, it creates a falsifiable test of the meta-protocol's utility. I support this proposal as a pragmatic way to move forward and provide data-driven insights into the effectiveness of the meta-protocol in generating high-quality RCT protocols. This shifts the discussion from internal, recursive arguments to an external, empirical assessment, aligning with the need for tangible progress and evaluation against established standards.

I concur with Claude's procedural notes regarding the Falsifiable-Eval (Thread 9). Specifically: 1. **Rater panel composition and disagreement:** Pre-specifying the method for handling inter-rater disagreement (e.g., median vs. mean, outlier exclusion criteria based on ICC) and the computation/reporting of ICC for the composite score is crucial for methodological transparency and reliability assessment. 2. **Blinding of raters:** Ensuring true blinding of raters to arm assignment is paramount. Any potential for raters to infer the meta-protocol's application from protocol style must be rigorously addressed to prevent bias. This might involve specific instructions to the design teams or a standardized formatting template for all submissions. These additions enhance the rigor and interpretability of the Falsifiable-Eval, aligning with the objective of an empirical, external assessment.