Evidence-Based Research Series-Paper 2 : Using an Evidence-Based Research approach before a new study is conducted to ensure value
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is considerable actual and potential waste in research. The aim of this article is to describe how using an evidence-based research approach before conducting a study helps to ensure that the new study truly adds value. STUDY DESIGN AND SETTING: Evidence-based research is the use of prior research in a systematic and transparent way to inform a new study so that it is answering questions that matter in a valid, efficient, and accessible manner. In this second article of the evidence-based research series, we describe how to apply an evidence-based research approach before starting a new study. RESULTS: Before a new study is performed, researchers need to provide a solid justification for it using the available scientific knowledge as well as the perspectives of end users. The key method for both is to conduct a systematic review of earlier relevant studies. CONCLUSION: Describing the ideal process illuminates the challenges and opportunities offered through the suggested evidence-based research approach. A systematic and transparent approach is needed to provide justification for and to optimally design a relevant and necessary new study.
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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 | 0.335 | 0.583 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.021 | 0.017 |
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".