PRESS survey: PREvention of surgical site infection—a global pan-specialty survey of practice protocol
Bibliographic record
Abstract
Background Surgical site infections (SSI) complicate up to 40% of surgical procedures, leading to increased patient morbidity and mortality. Previous research identified disparities in SSI prevention guidelines and clinical practices across different institutions. The study aims to identify variations in SSI prevention practices within and between specialties and financial systems and provide a representation of existing SSI preventative measures to help improve the standardization of SSI prevention practices. Methods This collaborative cross-sectional survey will be aimed at pan-surgical specialties internationally. The study has been designed and will be reported in line with the CROSS and CHERRIES standards. An international study steering committee will design and internally validate the survey in multiple consensus-based rounds. This will be based on SSI prevention measures outlined in the CDC (2017), WHO (2018), NICE (2019), Wounds UK (2020) and the International Surgical Wound Complications Advisory Panel (ISWCAP) guidelines. The questionnaire will include demographics, SSI surveillance, preoperative, peri-operative and postoperative SSI prevention. Data will be collected on participants' surgical specialty, operative grade, of practice and financial healthcare system of practice. The online survey will be designed and disseminated using QualtricsXM Platform™ through national and international surgical colleges and societies, in addition to social media and snowballing. Data collection will be open for 3 months with reminders, and raking will be used to ascertain the sample. Responses will be analyzed, and the chi-square test used to evaluate the impact of SSI prevention variables on responses. Discussion Current SSI prevention practice in UK Vascular surgery varies considerably, with little consensus on many measures. Given the inconsistency in guidelines on how to prevent SSIs, there is a need for standardization. This survey will investigate the disparity in SSI preventative measures between different surgical fields and countries.
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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".