Trends in otolaryngology research during the period 1995‐2000: A bibliometric approach
Bibliographic record
Abstract
OBJECTIVES: To evaluate the distribution and scope of papers published in the world in otolaryngology (ORL) journals and to compare the impact of this research among different countries. METHODS: Papers published in the 29 ORL journals screened by the Institute for Scientific Information (ISI, Philadelphia, PA, USA) in the 6-year period 1995-2000 were considered. The journal impact factor (IF), the source country population, and gross domestic product (GDP) were recorded. All key words, both those assigned by the authors and those attributed by ISI, were identified and their frequency was calculated using a special-purpose program. RESULTS: The total number of papers in the ORL literature during the period 1995-2000 increased from 2036 to 3705. A percentage varying between 47.7% (1995) and 36.1% (2000) was published by EU authors whereas the USA accounted for a percentage varying between 28.1% (1995) and 38.8% (2000). In 2000, the leading countries were the USA, the EU, Japan, Canada, and Australia. In Europe the UK (28.5% of papers), Germany (26.2%), Italy (7.2%), Sweden (5.8 %), France (5.5%), and the Netherlands (4.9%) showed a very good performance trend. In the same year, the mean IF of EU papers was 0.8 in comparison with 1.1 for Australia and the USA and 0.9 for the world. In 1997, 1341 key words attributed by the authors and 696 attributed by ISI appeared in the ORL literature. Less than a tenth of them were cited more than twice. The leading key words were "cancer" for disease and "surgery" for treatment. CONCLUSIONS: Bibliometric findings are useful to follow research trends. Our data show high scientific production of relatively small countries. Dispersion of key words should be avoided and journal editors should promote their standardization.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.099 | 0.136 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".