Evidence-based Radiology: Steps 1 and 2—Asking Answerable Questions and Searching for Evidence
Bibliographic record
Abstract
Evidence-based medicine originated at McMaster University, Hamilton, Ontario, Canada, where it was defined as "the integration of current best evidence with clinical expertise and patient values" by the Evidence-based Medicine Working Group led by Drs Gordon Guyatt and David Sackett. From this developed the McMaster University and National Health Service Centre for Evidence-Based Medicine, University of Oxford, paradigm of evidence-based practice, which consists of five steps that can be used by ordinary practitioners: formulate answerable questions with which to search for evidence, search the literature, appraise the retrieved evidence by using explicit methods, apply results to a patient or patient group, and evaluate one's evidence-based practice and clinical performance and practice. This communication is about the first two steps of this process. Step 1 provides a framework for more effective question formulation that improves subsequent literature searches. It works equally well for questions about diagnostic and interventional radiology. A clinical scenario for a diagnostic question is used to illustrate the formulation of an answerable question. This question is then used to illustrate step 2-how and where to search for evidence.
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 | 0.101 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".