System dynamics simulation for evaluating implementation strategies of genomic sequencing: tutorial and conceptual model
Bibliographic record
Abstract
INTRODUCTION: Precision Medicine (PM), especially in oncology, involve diagnostic and complex treatment pathways that are based on genomic features. To conduct evaluation and decision analysis for PM, advanced modeling techniques are needed due to its complexity. Although System Dynamics (SD) has strong modeling power, it has not been widely used in PM and individualized treatment. AREAS COVERED: We explained SD tools using examples in cancer context and the rationale behind using SD for genomic testing and personalized oncology. We compared SD with other Dynamic Simulation Modelling (DSM) methods and listed SD's advantages. We developed a conceptual model using Causal Loop Diagram (CLD) for strategic decision-making in Whole Genome Sequencing (WGS) implementation. EXPERT OPINION: The paper demonstrates that SD is well-suited for health policy evaluation challenges and has useful tools for modeling precision oncology and genomic testing. SD's system-oriented modeling captures dynamic and complex interactions within systems using feedback loops. SD models are simple to implement, utilize less data and computational resources, and conduct both exploratory and explanatory analyses over time. If the targeted system has complex interactions and many components, deals with lack of data, and requires interpretability and clinicians' input, SD offers attractive advantages for modeling and evaluating scenarios.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".