Evaluation of New Zealand's Radiology, Nuclear Medicine, and Medical Imaging Research Output: A Bibliometric‐Based Approach
Bibliographic record
Abstract
INTRODUCTION: The use of medical imaging services has increased globally with a concurrent increase in radiology, nuclear medicine and medical imaging (RNMI) research. However, New Zealand's RNMI research output relative to global trends is under-examined. This project evaluates New Zealand's RNMI research output between 1996 and 2022 compared to selected countries while highlighting global RNMI research output trends. METHODS: A bibliometric-based performance analysis was conducted using publication data from the SCImago Journal, the Country Rank portal, Clarivate InCites Benchmarking, and the Analytics platform. Registration data of RNMI professionals by country was collected to evaluate the relationship between research output and the number of registered professionals. RESULTS: Among the seven selected countries (the United States, United Kingdom, Canada, Australia, Ireland, New Zealand and South Africa), New Zealand's research output was low, even when adjusted for population size and the number of professionals. A significant positive correlation was found between the number of registered RNMI professionals and the number of RNMI publications. Despite this, New Zealand had the highest percentage of RNMI documents cited. CONCLUSION: Although New Zealand's RNMI publications follow the global upward trend, it does so at a proportionate loss. New Zealand ranked low in most bibliometric indicators apart from the percentage of documents cited, where it showed a notable citation impact. Emphasising research, increasing collaborative efforts, and undertaking further statistical analyses may enhance New Zealand's RNMI research output.
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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 | 0.028 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.076 | 0.122 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".