The 100 Most Cited Papers in the History of the American Surgical Association
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine characteristics of the most cited publications in the history of the American Surgical Association (ASA). SUMMARY BACKGROUND DATA: The Annals of Surgery has served as the journal of record for the ASA since 1928, with a special issue each year dedicated to papers presented before the ASA Annual Meeting. METHODS: The top 100 most cited ASA publications in the Annals of Surgery were identified from the Scopus database and evaluated for key characteristics. RESULTS: The 100 most cited papers from the ASA were published between 1955 and 2010 with an average of 609 citations (range: 333-2304) and are included among the 322 most cited papers in the Annals of Surgery. The most common subjects of study included clinical cancer (n = 43), gastrointestinal (n = 13), cardiothoracic/vascular (n = 9), and transplant (n = 9). Ninety-three institutions were included lead by Johns Hopkins University (n = 9), University of Pittsburgh (n = 8), Memorial Sloan-Kettering (n = 7), John Wayne Cancer Institute (n = 7), University of Texas (n = 7), and 5 each from Brigham and Women's Hospital, Mayo Clinic, and University of Chicago. The majority of manuscripts came from the United States (n = 85), followed by Canada (n = 7), Germany (n = 5), and Italy (n = 5). Study design included randomized controlled trials (n = 19), retrospective matched cohort studies (n = 11), retrospective nonmatched studies (n = 46), and other (n = 24). CONCLUSIONS: The top 100 most cited publications from the ASA are highly impactful, landmark studies representing a diverse array of subject matter, investigators, study design, institutions, and countries. These influential publications have immensely advanced surgical science over the decades and should serve as inspiration for all surgeons and surgical investigators.
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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 | 0.011 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.052 | 0.072 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".