Towards Equitable Access: Evaluating the Paediatric Interventional Radiology (PIR) Landscape in the Asia-Pacific Region
Bibliographic record
Abstract
## Introduction To evaluate the current state of PIR workforce, opportunities and challenges in the Asia- Pacific (APAC) region. ## Materials and Methods A three-part electronic survey was distributed to members of the Society of Pediatric Interventional Radiology (SPIR), Asian Oceanian Society for Pediatric Radiology (AOSPR), and Asia-Pacific Society of Cardiovascular and Interventional Radiology (APSCVIR). Part 1 addressed individual provider practices, Part 2 focused on institutional service, and Part 3 assessed perceptions on priorities for developing PIR in APAC, with respondents rating the importance of different factors on a 10-point scale. ## Results A total of 116 individual and 100 institutional responses from 19 APAC countries/regions were analysed. Only 11.2% (13/116) individuals identified as paediatric interventional radiologists, while the majority (66.3%, 77/116) were adult interventional radiologists. Only 6.0% (7/116) reported performing PIR in >50% of their clinical practice. Among the responding institutions, 28.0% (28/100) were children’s hospitals. Over half (52/100) have dedicated anaesthesia sessions for PIR. Common procedures included drainage (84%), biopsy (76%), vascular access (70%), and vascular anomaly treatment (70%). While all centres offered daytime PIR services, only 33.0% (33/100) provided on-call coverage. Among those, the majority (25/33) relied on a single PIR provider for on-call coverage. The highest-rated needs for PIR development were a specialist network for case discussion (mean score = 8.9), dedicated anaesthesia resources (8.7), and regional PIR training centres (8.5). ## Discussion PIR in the APAC region faces significant challenges in manpower and resource allocation. Strengthening structured training pathways and securing dedicated anaesthesia support are key to provide equitable PIR access across the region.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".