A Bibliometric Keyword Analysis of Articles Published in the Journal of the American College of Surgeons over 32 Years: A Changing Surgical Publication Focus
Bibliographic record
Abstract
Introduction: The Journal of the American College of Surgeons (JACS) publishes peer-reviewed original articles relevant to all aspects of surgery. This study aims to perform a bibliometric keyword analysis of articles published in JACS to identify areas of focus and trends in published surgical research. Methods: A list of all articles published in JACS between January 1, 1990 and January 1, 2022 was obtained from the Web of Science Core Collection. The full records and cited references of these articles were extracted and imported into VOSViewer software (Version 1.6.18) and analyzed. Co-occurrence of keywords within the articles were determined, and the top 100 keywords were extracted and analyzed. Results: A total of 20,275 articles published were identified and exported. The 10 most commonly occurring keywords found were: surgery (n = 748), management (n = 544), mortality (n = 481), outcomes (n = 475), cancer (n = 427), survival (n = 386), complication (n = 341), resection (n = 323), carcinoma (n = 296) and impact (n = 276). Analysis of keyword trends by average publication year demonstrate a shift in focus of the published research from surgical management to surgical outcomes. However, the study of surgical complication and survival have remained key areas of interest throughout the entire evaluated publication period. Conclusion: Bibliometric analysis of keyword co-occurrence from articles published in the JACS over the past 32 years is a novel method for assessing research trends in surgery. Recent shifts and ongoing areas of importance in surgical publication focus, as well as subjects that have received less attention, were also identified.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.169 | 0.187 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".