Finding Evidence-based Answers to Practical Questions in Radiology: Which Patients with Inoperable Hepatocellular Carcinoma Will Survive Longer after Transarterial Chemoembolization?
Bibliographic record
Abstract
To some, evidence-based practice (EBP) means the identification of centers that produce evidence reports and technology assessments to support guideline development. To others, EBP is the best research evidence integrated with clinical expertise and patient values. Inherent in the first approach is the implication that only central academic organizations can produce valid, reliable analyses of existing literature, which will then be distributed to ordinary practitioners. The second approach implies that ordinary practitioners can learn to use a stepwise approach and a preprepared set of rules and tools to effectively find the best current literature, appraise it, and then apply local circumstances to these rules and tools in their hospital. Paul Glasziou, director of the Centre for Evidence-based Practice in Oxford, England, has coined the phrases top-down EBP and bottom-up EBP to describe these approaches. In this article, the authors describe how knowledge gaps in an ordinary radiology practice can be addressed by using stepwise bottom-up EBP techniques. The following clinical scenario is used: Your hospital's recently appointed chief hepatobiliary surgeon questions the use of transarterial chemoembolization for inoperable hepatocellular carcinoma because of his concerns after reading a recent review article suggesting that there is no clear survival benefit to using this procedure. What would you do? Here is how the authors would do it.
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 | 0.052 | 0.268 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".