Clinical Decision Rules for Diagnostic Imaging in the Emergency Department: A Research Agenda
Bibliographic record
Abstract
BACKGROUND: Major gaps persist in the development, validation, and implementation of clinical decision rules (CDRs) for diagnostic imaging. OBJECTIVES: The objective of this working group and article was to generate a consensus-based research agenda for the development and implementation of CDRs for diagnostic imaging in the emergency department (ED). METHODS: The authors followed consensus methodology, as outlined by the journal Academic Emergency Medicine (AEM), combining literature review, electronic surveys, telephonic communications, and a modified nominal group technique. Final discussions occurred in person at the 2015 AEM consensus conference. RESULTS: A research agenda was developed, prioritizing the following questions: 1) what are the optimal methods to justify the derivation and validation of diagnostic imaging CDRs, 2) what level of evidence is required before disseminating CDRs for widespread implementation, 3) what defines a successful CDR, 4) how should investigators best compare CDRs to clinical judgment, and 5) what disease states are amenable (and highest priority) to development of CDRs for diagnostic imaging in the ED? CONCLUSIONS: The concepts discussed herein demonstrate the need for further research on CDR development and implementation regarding diagnostic imaging in the ED. Addressing this research agenda should have direct applicability to patients, clinicians, and health care systems.
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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 | 0.564 | 0.671 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.024 | 0.030 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".