The ‘Sic Vos non Vobis’ of Interventional Radiology – Rebranding and modernising the Interventional Specialities of Radiology in Australia and New Zealand
Bibliographic record
Abstract
Point of viewSic Vos Non Vobis ('For You, But Not Yours') were the words Vergil wrote on Emperor Augustus' palace doorpost, when Bathyllus, another poet, had plagiarised his work lavishing praise on emperor Augustus. 1 In his famous retribution, he quipped the bees do not produce the honey for themselves, but for others.But maybe it is time for a change?Interventional radiology (IR) and interventional neuroradiology (INR) have a recognition and branding problem.There is confusion about their identity not only amongst the public but also amongst our medical and surgical colleagues.2,3 Even amongst radiologists, knowledge of the interventional specialties can be limited.Often, it is not realised that not all interventional radiologists provide the same service.These challenges have implications at many levels: future trainees cannot be inspired without appropriate knowledge of the specialties; challenges exist with accreditation and training; and advocacy of our profession for patients will ultimately suffer.4 The purpose of this opinion piece is to stimulate a discussion and reach a consensus on how we view
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".