Review article: A primer for clinical researchers in the emergency department: Part IX. How to conduct a systematic review in the field of emergency medicine
Bibliographic record
Abstract
In this series we address important topics for emergency clinicians who either participate in research as part of their work, or use the knowledge generated by research studies. Emergency clinicians are routinely in the position of applying new evidence in clinical practice. With an ever-increasing volume of evidence generated, this can be problematic when studies are conducted in different settings, and include different patient groups, different interventions and different outcomes. This is made even more difficult when the results of primary research studies do not agree. Systematic reviews are becoming increasingly valuable as they appraise and synthesise research findings using a clear methodology, and summarise the results of primary studies. As such, systematic reviews help translate research findings into clinical practice. This paper provides a practical starting point for understanding the steps involved in conducting a systematic review in emergency medicine and will help readers appraise the findings of systematic reviews.
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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 | 0.134 | 0.320 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.027 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".