Effective Synthesized/preappraised Evidence Formats in Emergency Medicine and the Use of Supplemental Knowledge Translation Techniques
Bibliographic record
Abstract
Most clinicians, and especially emergency physicians, are increasingly faced with the need for valid and reliable evidence upon which to base practice decisions in a timely fashion. Despite the accumulation of synthesized evidence in emergency medicine over the past decade, knowledge gaps still exist between what is known and what is practiced. In many cases, this failure in knowledge uptake relates to barriers in uptake as well as the difficulty of translating evidence from research to the bedside. Preappraised evidence syntheses represent a potential partial solution to these problems by providing condensed summaries of the large volume of scientific literature in our field. The participants in this workshop examined the availability, utility, and impact of preappraised evidence and examined innovative ways to translate this knowledge into practice. In addition, the workshop participants also explored more globally all knowledge translation methods that are distinct from clinical pathways (e.g., audit and feedback, academic detailing, reminders, and local opinion leaders). These are initiatives that are instituted at the level of a particular hospital or with respect to a certain condition, and emergency physicians need to understand their definition and application. Overall, the recommendations arising from this workshop have the potential to alter future emergency care in important ways.
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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 | 0.406 | 0.624 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".