One Hundred Citation Classics in General Surgical Journals
Bibliographic record
Abstract
The number of times an article is cited in scientific journals reflects its impact on a specific biomedical field or specialty and reflects the impact of the authors' creativity. Our objective was to identify and analyze the characteristics of the 100 most frequently cited articles published in journals dedicated to general surgery and its close subspecialties. Using the database (1945-1995) of the Science Citation Index of the Institute for Scientific Information, 1500 articles cited 100 times and more were identified and the top 100 articles selected for further analysis. The 100 articles were published between 1931 and 1990, with more than two-thirds of them published after 1960. The mean number of citations per article was 405, (range 278-1013). Altogether, 84 of the articles originated from North America (USA 78, Canada 6) and the UK (12). New York State led the list of U.S. states with 14, and Harvard and Columbia University led the list of institutions with 6 articles each. The 100 articles were published in 10 surgical journals led by the Annals of Surgery (n = 40), followed by Surgery (n = 15), Archives of Surgery (n = 12), Surgery, Gynecology and Obstetrics (n = 11), and British Journal of Surgery (n = 10). A total of 80 of the articles reported clinical experiences, 6 were clinical review articles, and 14 dealt with basic science. Eighteen articles reported a new surgical technique and six a prosthetic device. Gastrointestinal surgery and trauma and critical care led the list of the surgical fields, each with 25 articles, followed by vascular surgery (n = 15). Thirty-four persons authored two or more of the top-cited articles. This list of the top-cited papers identifies seminal contributions and their originators, facilitating the understanding and discourse of modern surgical history and offering surgeons hints about what makes a contribution a "top-cited classic." To produce such a "classic" the surgeon and his or her group must come up with a clinical or nonclinical innovation, observation, or discovery that has a long-standing effect on the way we practice-be it operative or nonoperative. Based on our findings, to be well cited such a contribution should be published in the English language in a high-impact journal. Moreover, it is more likely to resonant loudly if it originates from a North American or British "ivory tower."
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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.108 | 0.140 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".