Standardised Reporting Outcomes for trials investigating Surgical WOund Complications (ROSWOC); protocol for the development of a core outcomes set for trials evaluating interventions for the prevention of surgical wound complications.
Bibliographic record
Abstract
Despite advances in surgical technique and wound management surgical wound complications such as surgical site infection and surgical wound dehiscence still pose a considerable global burden. Inconsistencies in measuring and reporting of this phenomenon pervade study designs, analysis and synthesis of the evidence, as such a core outcome set (COS) is required. The Reporting Outcomes for Surgical WOund Complications project (ROSWOC) aims to improve quality of reporting and evidence for surgical site infection and surgical wound dehiscence prevention trials. A COS for trials is required to homogenise outcomes for trials investigating prevention and management of surgical wound complications. Methods/design: This project aims to develop a core outcomes set following established methods; 1) define scope of work, 2) conduct a scoping review, 3) organising facilitated workshops with service users and 4) conduct Delphi surveys, and 5) conduct face-to-face meetings with key stakeholders. Discussion: Following obtaining consensus for the core set, further work will be carried out to describe a core measuring set. The articulation of an agreed set of core outcomes for trials investigating surgical site infections and surgical wound dehiscence will improve prevention studies into surgical wound complications into the future. Trial registration: The ROSWOC project is registered in the COMET database: (http://www.comet-initiative.org/studies/details/) registered November 2022.
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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 | 0.387 | 0.437 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.052 | 0.023 |
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".