A Generalizable Distribution Structure Analysis Algorithm with Audit-Ready Framework for Medical Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Background Conventional statistical methods in medical research often fail to capture real-world complexity due to rigid parametric assumptions, particularly normality, which frequently do not hold for clinical and epidemiological data. Heterogeneous distributions, heavy-tailed patterns, and multimodal structures are common in healthcare data, yet conventional methods often fail to capture these structural characteristics, leading to information loss and potentially misleading conclusions. Furthermore, regulatory audits and reproducibility requirements demand transparent, traceable analytical frameworks. Objective This study presents a comprehensive Distribution Structure Analysis (DSA) algorithm with an integrated audit-ready framework designed specifically for medical research. The algorithm systematically identifies distributional structures, ensures statistical rigor through explicit estimand specification and goodness-of-fit testing, and maintains complete audit trails for regulatory compliance. Methods The DSA algorithm integrates five key components: (1) explicit estimand specification aligned with research design, (2) automated distribution type identification (normal, log-normal, exponential, Weibull, power-law, and mixture models), (3) comprehensive goodness-of-fit assessment using multiple criteria (AIC/BIC, visual diagnostics, and statistical tests), (4) causal inference support through Directed Acyclic Graphs (DAG), and (5) automated audit logging with a three-tier quality control system (red/yellow/green). The algorithm was validated using both simulated datasets with known distributions and real-world medical data from clinical trials and epidemiological studies. Results Validation studies demonstrated that the DSA algorithm correctly identified distribution types with 95% accuracy across 1,000 simulated datasets. In clinical trial data analysis, the algorithm detected heavy-tailed distributions in adverse event frequencies that were missed by conventional normality-based methods, leading to more accurate safety assessments. The audit logging system successfully recorded all analytical decisions, enabling complete reproducibility. The three-tier quality control system flagged 12% of analyses for re-examination, preventing potential methodological errors. Application to epidemiological data revealed multimodal patterns in disease incidence that informed targeted public health interventions. Conclusions The DSA algorithm with integrated audit-ready framework provides a rigorous, transparent, and reproducible approach to distribution structure analysis in medical research. By explicitly addressing estimands, ensuring goodness-of-fit, and maintaining complete audit trails, the framework meets both statistical rigor and regulatory compliance requirements. The algorithm is applicable across diverse medical research domains, including clinical trials, epidemiology, health economics, and pharmacovigilance. Open-source implementation and comprehensive documentation facilitate adoption and validation by the research community.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it