Guidelines for Authors and Reviewers of <b> <i>Plastic Surgery</i> </b>
Bibliographic record
Abstract
Credible clinical research is a precondition of evidence-based surgery. If clinical research is not conducted and reported properly, such research can be unreliable, unclear, and misleading. Our journal, Plastic Surgery, aims to improve its quality and thus enhance interest, submissions, and readership. To do so, we must ensure that the articles published in our journal align with these goals. This article guides future clinical research contributors, how to design, conduct and report valuable and reliable research. Readers are informed how to choose a title and keywords that properly reflect the content of the article. The proper organization of a manuscript, and the information that goes into each section is described. Valuable tools like the EQUATOR Network Guidelines, the FINER Criteria and the PICOT Format are described for the reader. These resources help formulate a proper research question and ensure transparency in reporting. Commonly used study designs, and the research questions they answer are presented. This ensures that those engaged in research are choosing the right study design for their research. We outline the statistical information that should be presented in the Methods section and differentiate between the content that should be found in the Results and Discussion sections. As Plastic Surgery strives to publish high-quality, reliable research, it is by the standards presented in this article that we will judge all manuscripts submitted for publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.342 | 0.752 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.023 | 0.018 |
| Insufficient payload (model declined to judge) | 0.070 | 0.115 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".