Publication Rates and Author Characteristics From 3 Plastic Surgery Journals in 2006 and 2016
Bibliographic record
Abstract
BACKGROUND: Areas of surgical care in which, traditionally, plastic surgeons were exclusively involved are now routinely offered by other surgical specialists. Whether this shift in clinical responsibilities influenced publication rates of plastic surgeons remains unknown. The current article investigates the proportion of contributions in plastic surgery journals originating from authors with a plastic surgery background as well as publication rates and author demographics. METHODS: A cross-sectional sample study of every publication originating from Annals of Plastic Surgery, Journal of Plastic, Reconstructive and Aesthetic Surgery, and Plastic and Reconstructive Surgery was performed for 2006 and 2016. Data about the articles' methodological design and branch of plastic surgery as well as authors' country of origin, educational degree and specialty training were analyzed. RESULTS: From 1721 publications included, head and neck reconstruction was the branch of plastic surgery with the highest number of publications at 18% and most articles (30%) were retrospective cohort studies. From 3381 authors analyzed, a significant proportion originated from United States (34%). More than 85% of authors were physicians as opposed to other health care professionals. The specialty with the highest representation was plastic surgery at 72%, but the proportion decreased in all 3 journals by a mean rate of 3.8% in 2016. CONCLUSIONS: A slight decrease in publication rates from plastic surgeons occurred in Annals of Plastic Surgery, Journal of Plastic, Reconstructive and Aesthetic Surgery, and Plastic and Reconstructive Surgery from 2006 to 2016. Publications rates and author characteristics in plastic surgery journals provide valuable insight on plastic surgeons' contribution to contemporary scientific literature.
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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 | 0.016 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.038 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".