Australian contribution to global otolaryngology research: 2008–2018
Bibliographic record
Abstract
BACKGROUND: Obtaining research funding in Otolaryngology - Head and Neck Surgery (ORL-HNS) can be challenging. In this paper, an analysis of research output in ORL-HNS in Australia and worldwide between 2008 and 2018 was conducted and then adjusted for the number of specialist surgeons in each country. METHODS: Scopus by Elsevier was used to measure research output of Ear, Nose and Throat (ENT) surgeons in Australia between 2008 and 2018. Each individual's career and 10-year h-index was identified and then repeated with self-citations excluded. Total and 10-year citations were also recorded. The top 15 countries in terms of research output in ORL-HNS were also ranked based on the number of ENT articles published in the 10-year period, and then adjusted by the number of actively practicing ENT surgeons per country. RESULTS: Between 2008 and 2018, Australia published 1510 articles out of a total global output of 48 613 papers in ORL-HNS with the top 10 authors having an h-index placing them within the world's top 100. Whilst the USA made the greatest total contribution with 12 912 publications, when adjusted for the number of specialist ORL-HNS clinicians in each country, Australia, Canada and India in order topped the rankings. CONCLUSION: Australia has established itself as a research leader in the global field of ORL-HNS, publishing more articles per surgeon than any other country between 2008 and 2018. The Australian ORL-HNS Academic Surgeons also rank highly on an individual basis when compared to global peers.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".