Need for Speed: Investigating Publication Times and Impact Factors of Plastic Surgery Journals
Bibliographic record
Abstract
BACKGROUND: Prolonged publishing time in scientific journals can be discouraging for researchers because earlier publication can mean a higher h-index and more academic opportunities. In this study, we evaluated the publication time for articles in plastic surgery journals compared with journals in surgery and medicine. We also assessed correlations between publication speed and journal impact factors (IFs). METHODS: The overall indexes of all plastic surgery journals were compared with journals in the discipline of surgery and medicine. In addition, we evaluated original articles published in all plastic surgical journals and the highest-ranking journals from various surgical subspecialties listed in the 2018 Journal Citation Report, assessing the time intervals from submission to publication, submission to acceptance, and acceptance to publication. Correlation between time interval and journal IF were analyzed. RESULTS: < 0.05, Wilcoxon test). The median submission-to-publication time for all plastic surgery and all surgical journals was 29.7 weeks (IQR, 12.1 and 35.8) and 22.1 days (IQR,18.8 and 36.8), respectively. CONCLUSIONS: There is a significant submission to publication time lag in plastic surgery journals when compared with other nonplastic-surgery journals. There was a positive correlation between submission-to publication time and IF for plastic surgery journals but a negative correlation for surgery journals (Spearman Correlation). In the last 14 years, plastic surgery journals have remained slow in publishing articles.
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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 | 0.017 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".