Data_Sheet_1_Antimicrobial Stewardship in Surgery: A Literature Bibliometric Analysis.PDF
Bibliographic record
Abstract
Background<p>Antimicrobial resistance and the dwindling antibiotic development pipeline have resulted in a looming post-antibiotic era. Research related to antimicrobial stewardship (AMS) has grown rapidly in the past decade, especially in the field of surgery. We conducted a bibliometric analysis of these publications. In addition, we aimed to identify research hotspots and infer future research trends.</p>Methods<p>We screened global publications on AMS in the surgical field over ten years (between 2011 and 2020) from the Web of Science core collection database. The keywords “antimicrobial or antibiotic”, “stewardship”, “management”, “management strategies”, “programme”, “surgery” and “surgical” were used to search for related papers. VOS viewer, R software, and other machine learning and visualization tools were used to conduct the bibliometric analysis of the publications.</p>Results<p>We identified 674 publications on AMS in surgical fields; “antimicrobial stewardship” (with total link strength of 1,096) was the most frequent keyword, and had strong links to “antimicrobial resistance” and “guidelines”. The top 100 most cited papers had a mean citation count of 47.21 (range: 17–1155) citations, which were cited by survey research studies, clinical trials, and observational studies. The highest-ranking and most cited journal was Clinical Infectious Diseases with eight publications. Jason G. Newland from Washington University wrote seven papers and was cited 1,282 times. The University of Washington published 17 papers and was cited 1,258 times, with the largest number of publications by author and organization. The USA published 198 papers and cooperated with 21 countries, mainly partnering with Italy, the UK, and Canada. Published articles mainly focused on the current clinical situation regarding surgical AMS management, antibiotic prescription, and antibiotic resistance.</p>Conclusions<p>Publications on surgical AMS management have increased in recent decades, with the USA being the most prolific. Epidemiological investigations of surgical-related infections, antibiotic prescriptions, and antibiotic resistance are fast-developing research trends. However, further improvements are still needed according to the recommendations gained from the bibliometric analysis.</p>
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.488 | 0.714 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.983 | 0.105 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".