Systematic reviews in emergency medicine: Part I. Background and general principles for locating and critically appraising reviews
Bibliographic record
Abstract
Reviews of the medical literature have always been an important resource for physicians. Increasingly, qualitative and quantitative "systematic reviews" have replaced the traditional "narrative review" as a means of capturing and summarizing current evidence on a topic or, when possible, answering a specific clinical question. This paper is part one of a two-part series designed to provide emergency physicians with the background necessary to locate, critically evaluate and interpret systematic reviews. The paper provides a brief background on systematic reviews and general principles on locating and critically appraising them. To facilitate readability, examples from the emergency medicine literature have been included for illustrative purposes and technical details have been kept to a minimum. The references, however, are comprehensive and provide a resource for readers seeking further information.
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 | 0.120 | 0.178 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".