PP036 Topic: AS04–Emerging Sciences, Methodologies, Big Data and Technology: DIRECTED ACYCLIC GRAPHS, AN EPIDEMIOLOGICAL METHOD TO IMPROVE STUDY DESIGN IN PEDIATRIC CRITICAL CARE RESEARCH
Bibliographic record
Abstract
Aims & Objectives: Early, optimal, enteral nutrition (EN) has been shown to reduce morbidity and mortality in pediatric critical illness. Interventions aimed at improving EN delivery such as Volume-based enteral nutrition (VBEN) need to be studied to determine the true effect of intervention(X) on outcome(Y). Directed Acyclic Graphs (DAGs) graphically represent a causal relationship. Arrows connect variables representing causal links and cannot loop back on themselves. DAGs display pathways from exposure to outcome while including other relevant variables making them useful tools to identify variable inclusion for analysis plans. Objective: Explore the possible causal relationship between VBEN and PICU morbidity/mortality using a DAG. Methods: Utilize existing literature and knowledge to create a DAG of the proposed relationship between VBEN and morbidity/mortality. (Figure 1)Results: This DAG identifies that VBEN may improve morbidity/mortality directly (pathway a) or indirectly by improving enteral energy and protein intake (pathway b). Therefore, in order to understand the total effect of VBEN on outcomes there should be no adjustment for adequacy of nutrition delivery. Severity of illness is identified as being associated with both exposure and outcome (pathway c and d) and is a potential confounder that should be included in any adjusted analysis. Conclusions: This DAG provides a qualitative frame of the causal structure of VBEN and morbidity/mortality. Given their reliance on prior knowledge DAGs are only as accurate as the assumptions underlying them. Although in the absence of strong background data DAGs may be prone to misspecification, a well-informed DAG supports correct variable identification to inform study design and analysis. Keywords: epidemiology, Directed Acyclic Graph, Randomized Controlled Trial, Nutrition
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".