The Practice of Ethics in the Era of Evidence-based Radiology
Bibliographic record
Abstract
Various methods to provide an ethical conscience in the modern practice of radiology are available, but they all require time and effort. Although this is part of a series dedicated to evidence-based radiology (EBR), this article cannot provide recommendations supported by evidence. However, the method we propose is inspired from the analytic process found in EBR. It emphasizes autonomous reflection; systematic identification of roles, motives, and consequences of current actions and organizations; clarification of aims and means; selection of principles and values; and equilibration, application, and validation. This personal process is followed by the search for common values shared by the group, in a rational, scientific context centered on preserving the patient-physician relationship. This method entails constant vigilance and repeated revisions, but the allowance of time to think about ethical matters can decrease confusion and moral perplexity. The result is a stronger moral personal identity and a brighter horizon for a satisfying professional life.
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 | 0.158 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.132 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.019 | 0.041 |
| Insufficient payload (model declined to judge) | 0.002 | 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".