Consensus Minimal Dataset for Pediatric Emergency Medicine in Switzerland
Bibliographic record
Abstract
OBJECTIVES: Standardized, harmonized data sets generated through routine clinical and administrative documentation can greatly accelerate the generation of evidence to improve patient care. The objective of this study was to define a pediatric emergency medicine (PEM) minimal dataset for Switzerland (Swiss PEM minimal dataset) and to contribute a subspecialty module to a national pediatric data harmonization process (SwissPedData). METHODS: We completed a modified Delphi survey, inviting experts from all major Swiss pediatric emergency departments (PEDs). RESULTS: Twelve experts from 10 Swiss PEDs, through 3 Delphi survey rounds and a moderated e-mail discussion, suggested a subspecialty module for PEM to complement the newly developed SwissPedData main common data model (CDM). The PEM subspecialty CDM contains 28 common data elements (CDEs) specific to PEM. Additional CDEs cover PEM-specific admission processes (type of arrival), timestamps (time of death), greater details on investigations and treatments received at the PED, and PEM procedures (eg, procedural sedation). In addition to the 28 CDEs specific to PEM, 43 items from the SwissPedData main CDM were selected to create a Swiss PEM minimal dataset. The final Swiss PEM minimal dataset was similar in scope and content to the registry of the Pediatric Emergency Care Applied Research Network. CONCLUSIONS: A practical minimal dataset for PEM in Switzerland was developed through recognized consensus methodology. The Swiss PEM minimal dataset developed by Swiss PEM experts will facilitate international data sharing for PEM research and quality improvement projects.
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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 | 0.034 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".