Evidence-Based Research Series-Paper 3: Using an Evidence-Based Research approach to place your results into context after the study is performed to ensure usefulness of the conclusion
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There is considerable actual and potential waste in research. Using evidence-based research (EBR) can ensure the value of a new study. The aim of this article, the third in a series, is to describe an EBR approach to putting research results into context. STUDY DESIGN AND SETTING: EBR 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 third and final article of a series, we describe how to use the context of existing evidence to reach and present a trustworthy and useful conclusion when reporting results from a new clinical study. RESULTS: We describe a method, the EBR approach, that by using a systematic and transparent consideration of earlier similar studies when interpreting and presenting results from a new original study will ensure usefulness of the conclusion. CONCLUSION: Using an EBR approach will improve the usefulness of a clinical study by providing the context to draw more valid conclusions and explicit information about new research needs.
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 | 0.252 | 0.491 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.015 |
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".