Best practices in emergency medicine: what we have to consider if we wish to get it right
Bibliographic record
Abstract
Abstract Purpose The purpose of this paper is to define best practice, while identifying the impediments to its implementation. Design/methodology/approach The paper takes the form of a commentary. Findings There is as of yet no accepted definition of best practice that has both face and construct validity. Practical implications Defining what best practice means for health systems around the world will require a collaborative approach, adapting recommendations to local culture and resources. Avoiding a silo approach that could result in unintended consequences and conflicting recommendations can only be achieved with a patient-centric approach. Holistic patient care with consideration of varying societies' needs as a whole is the only way to truly offer best practice recommendations. Emergency medicine needs to be a leader in stepping away from the silo approach and establishing what truly is best in patient care. Originality/value Practical application of concepts of best practice will be difficult. Of necessity they will vary from country to country and from one level of care to another.
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 | 0.209 | 0.384 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.035 | 0.059 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.029 | 0.050 |
| Insufficient payload (model declined to judge) | 0.008 | 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".