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Record W4389373691 · doi:10.1186/s13017-023-00526-3

Declaration on infection prevention and management in global surgery

2023· letter· en· W4389373691 on OpenAlexaff
Massimo Sartelli, Federico Coccolini, Luca Ansaloni, Walter L. Biffl, David Blake, Marja A. Boermeester, Raúl Coimbra, Heather L. Evans, Paula Ferrada, George Gkiokas, Marc G. Jeschke, Timothy Craig Hardcastle, Chandler Hinson, Francesco M. Labricciosa, Sanjay Marwah, Antonio Marttos, Martha Quiodettis, Kemal Raşa, Jianan Ren, Inés Rubio‐Pérez, Robert G. Sawyer, Vishal G. Shelat, Jeffrey S. Upperman, Fausto Catena

Bibliographic record

VenueWorld Journal of Emergency Surgery · 2023
Typeletter
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsMedicineDeclarationBattleSurgical InfectionsAllianceTrauma surgeryInfection controlIntensive care medicineSurgeryAntibioticsLawPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.288
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes1
Has abstractyes

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