The 100 Most-Cited Papers on Giant Cell Arteritis: A BibliometricAnalysis
Bibliographic record
Abstract
BACKGROUND: Giant cell arteritis (GCA) carries a significant risk of vascular and visual morbidity. Given its clinical importance, the 100 most frequently cited articles on GCA were systematically identified and bibliometrically analyzed. METHODS: All databases belonging to the Web of Science platform were searched for research articles with no restriction on publication date. The distribution of papers among journals, countries of origin, and publication types were evaluated. The correlations between the year of publication with total number of citations and annual citation rate were also assessed. RESULTS: The top 100 articles on GCA were published between 1946 and 2018 and were cited a median (range) of 229 (153-1751) times. The papers were published in 30 journals, including nine rheumatology journals (n= 45), seven general medical journals (n= 21), three ophthalmology journals (n= 8), and eleven journals from other fields of research (n= 26). Based on corresponding author affiliation, the articles originated from 13 countries, led by the US (n= 55), Spain (n= 12), and the UK (n= 11). Clinical studies (n= 73) and non-systematic reviews (n= 11) were the most common publication types. The median (range) number of authors per article was 5 (1-44), and 73 individuals had more than one authorship. Year of publication was significantly correlated with the annual citation rate (P<0.001) but not with the total number of citations (P= 0.487). CONCLUSION: This bibliometric analysis provides insight into the history and evolution of GCA research, highlighting some of the most influential contributions to the field. The latest landmark papers may not have been identified due to temporal constraints on citation accumulation.
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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.164 | 0.166 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".