A Bibliometric Analysis of the Most Cited Articles in Global Reconstructive Surgery
Bibliographic record
Abstract
BACKGROUND: A substantial global inequality exists between surgical need and the availability of safe, affordable surgical care. Low- and middle-income countries have the greatest burden of untreated surgical disease and addressing this inequity is the goal of the Global Surgery movement. Reconstructive surgery is a fundamental component of Global Surgery as it is central to the appropriate treatment of trauma, burns, wounds, and congenital malformations. The objective of this study was to analyze the most frequently cited articles in the field of global reconstructive surgery to understand the main publication trends. METHODS: The 25 most cited articles relating to global reconstructive surgery were identified from all available journals through the Web of Science online database. The following data were extracted from each included article: title, source journal, publication year, total citations, average citations per year, authors, main subject, reconstructive surgery subspecialty, country, and institution of origin. RESULTS: The average number of citations per article was 21.7 (median, 19; range, 10-40). Most articles originated from the United States, and only 1 originated from a low-income country. The majority of the articles focused on cleft lip and palate (CLP) (72%), with few articles discussing burns or trauma. The main discussion themes were the quality of care provided in low- and middle-income countries both by local and visiting teams, the burden of diseases in relation to global reconstructive surgery, and the impact of surgical interventions economically and on patients. CONCLUSIONS: The number of research articles and citations related to global reconstructive surgery are limited. Despite having a lower incidence than burns or trauma, there is a preponderance of reports focusing on missions treating CLP. These findings suggest that more research funding could be invested in global reconstructive surgery for conditions other than CLP.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.219 | 0.241 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".