Declaration on infection prevention and management in global surgery
Bibliographic record
Abstract
Abstract Surgeons in their daily practice are at the forefront in preventing and managing infections. However, among surgeons, appropriate measures of infection prevention and management are often disregarded. The lack of awareness of infection and prevention measures has marginalized surgeons from this battle. Together, the Global Alliance for Infections in Surgery (GAIS), the World Society of Emergency Surgery (WSES), the Surgical Infection Society (SIS), the Surgical Infection Society-Europe (SIS-E), the World Surgical Infection Society (WSIS), the American Association for the Surgery of Trauma (AAST), and the Panamerican Trauma Society (PTS) have jointly completed an international declaration, highlighting the threat posed by antimicrobial resistance globally and the need for preventing and managing infections appropriately across the surgical pathway. The authors representing these surgical societies call all surgeons around the world to participate in this global cause by pledging support for this declaration for maintaining the effectiveness of current and future antibiotics.
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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.021 | 0.039 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".