Ascertaining top evidence in emergency medicine: A modified Delphi study
Bibliographic record
Abstract
OBJECTIVES: The application of evidence-informed practice in emergency medicine (EM) is critical to improve the quality of patient care. EM is a specialty with a broad knowledge base making it daunting for a junior resident to know where to begin the acquisition of evidence-based knowledge. Our study's objective was to formulate a list of "top papers" in the field of EM using a Delphi approach to achieve an expert consensus. METHODS: Participants were recruited from all 14 specialty EM programs across Canada by a nomination process by the program directors. The modified Delphi survey consisted of three study rounds, each round sent out via email. The study tool was piloted first with McMaster University's specialty EM residents. During the first round, participants individually listed top papers relevant to EM. During the two subsequent rounds, participants ranked the papers listed in the first round, with a chance to adjust ranking based on group responses. RESULTS: A total of eight EM specialty programs responded with 30 responses across the three rounds. There were 119 studies suggested in the first round, and, by the third round, a consensus of>70% agreement was reached to generate the final list of 29 studies. CONCLUSIONS: We produced, via an expert consensus, a list of top studies relevant for Canadian EM physicians in training. It can be used as an educational resource for junior residents as they transition into practice.
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 | 0.161 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".