Bibliometric Analysis of Septoplasty: Trends, Influential Studies, and Global Contributions
Bibliographic record
Abstract
Septoplasty is a frequently performed nasal airway procedure, yet the influence and provenance of its scientific literature have not been systematically quantified. We searched Clarivate Web of Science for "septoplasty," retrieved 2,528 records, and ranked them by citations. The 50 most-cited English-language articles were analyzed for citation count, publication year, country, institutional affiliation, authorship frequency, and Oxford Level of Evidence (LOE). Regression tested whether article characteristics predicted citation volume. The 50 papers accumulated 5639 citations (mean ± SD=112.8 ± 57.8; range=68-383). Publication years spanned 1989 to 2018, with a peak of 5 influential papers in 2010 (626 citations). Four authors-Rhee, Most, Garcia, and Kimbell-each authored 4 of the top papers. The United States produced half of all articles (25) and 3158 citations, far exceeding Canada (4 papers, 405 citations) and Germany (4, 389). Institutions with the highest output were the Medical College of Wisconsin (11 papers, 1,468 citations) and the University of North Carolina at Chapel Hill (7, 829). LOE distribution favored retrospective cohort studies (level III: 34/50). Univariate analysis showed no citation association with LOE ( P =0.445), article length ( P =0.946), or publication year ( P =0.666), whereas country of origin was significant ( P =0.016); multivariate models confirmed higher citations for United States, Turkish, and Italian studies. These findings demonstrate that septoplasty scholarship is dominated by a small group of authors and US centers, relies largely on Level III evidence, and that geographic origin-not study design-best predicts citation impact. Targeted international collaboration and higher-quality prospective research are needed to diversify and strengthen future septoplasty literature.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.085 | 0.143 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".