Evidence-based Reviews and Databases: Are They Worth the Effort? Developing Evidence Summaries for Emergency Medicine
Bibliographic record
Abstract
A broad range of relevant evidence-based resources within and outside of emergency medicine (EM) collates and summarizes research evidence pertaining to many questions relevant to clinical emergency care. Such resources may or may not constitute the equivalent of health care recommendations, and their relationship to clinical decision-making may be complex. Many efforts in evidence-based medicine resource development, and their products, are marginally relevant to EM practice but may serve as useful models for parallel EM relevant efforts. A trade-off exists between synthesis quality and ease of practitioner access and use. Keeping all such resources up to date is a major challenge. Although observational evidence suggests that dynamic interactivity and information retrieval technology may enhance practitioner utilization, little evidence exists supporting the absolute or comparative effectiveness of different kinds of resources and databases in enhancing evidence uptake or changing clinician behavior.
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 | 0.394 | 0.769 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.010 |
| Bibliometrics | 0.037 | 0.040 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.023 | 0.039 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".