Foot osteoarthritis research: A bibliometric analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a bibliographic analysis of English language, non-surgical research pertaining to foot osteoarthritis (OA). DESIGN: A Scopus® database search was conducted to identify all non-surgical foot OA articles published in English up until December 2023. Bibliometric analysis was performed using an open-source tool based on the R language. Citations, journals, authors, institutions, and countries were described. Publications were manually categorised according to research type and funding source. RESULTS: The search strategy yielded 121 eligible articles, which received a total of 4531 citations and were published by 372 authors in 55 journals. The highest publication output occurred in the past decade. The most frequent journals were Arthritis Care & Research, Osteoarthritis and Cartilage, Journal of the American Podiatric Medical Association, Annals of the Rheumatic Diseases, Foot & Ankle International and Journal of Foot and Ankle Research. The most published institutions were Keele University, La Trobe University, the University of Leeds, the University of Melbourne and the University of North Carolina. Of the 106 articles that could be classified, most were focused on aetiology (n=52, 49%), followed by evaluation of treatments and therapeutic interventions (n=28, 26%), detection, screening and diagnosis (n=17, 16%), health and social care services research (n=6, 6%) and underpinning research (3, 3%). Fifty-one articles (42%) reported no research funding. CONCLUSION: Foot OA research has increased significantly in the past decade. There is a need, however, to improve our understanding of the condition and to address the relatively small number of clinical trials that have been conducted.
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 | 0.025 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.257 | 0.248 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".