NASPGHAN position statement: Enabling quality pediatric gastroenterology care through electronic health record data capture and visualization
Bibliographic record
Abstract
Clinical practice guidelines are structured recommendations, derived from evidence-based research, aiming to inform, improve, and standardize patient care. This position paper considers the critical role the electronic health record (EHR) plays in data collection and implementation of guidelines. We describe EHR functionalities necessary to make guidelines actionable within the EHR and provide overview of data storage to inform design of data capture tools to reduce overall clinician workload. After reviewing current knowledge and practices, we have formulated the recommendation that NASPGHAN committees should develop clinical guidelines that identify specific and relevant health assessment measures with strong validity evidence, including patient-reported outcome measures. Guidelines should also outline clinical pathways, incorporating clinical decision support algorithms to provide feedback to users, and order sets to ensure the right guidance is provided for the right patient at the right time. Patient populations should be defined by using standard code sets. Committees should identify disease-specific health assessment measures with strong validity of evidence and identify areas where measures are still needed. Committees should offer guidance on population-based disease management and data visualization tools.
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 | 0.105 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.029 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".