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Record W2611336392 · doi:10.1186/s13017-017-0132-7

Management of intra-abdominal infections: recommendations by the WSES 2016 consensus conference.

2017· book· en· W2611336392 on OpenAlexaff
Massimo Sartelli, Fausto Catena, Fikri M. Abu‐Zidan, Luca Ansaloni, Walter L. Biffl, Marja A. Boermeester, Marco Ceresoli, Osvaldo Chiara, Federico Coccolini, Jan J. De Waele, Salomone Di Saverio, Christian Eckmann, Gustavo Pereira Fraga, Maddalena Giannella, Massimo Girardis, Ewen A. Griffiths, Jeffry L. Kashuk, Vladimir Khokha, Yoram Kluger, Francesco M. Labricciosa, Ari Leppäniemi, Ronald V. Maier, Addison K. May, Mark A. Malangoni, Ignacio Martín‐Loeches, John E. Mazuski, Philippe Montravers, Andrew B. Peitzman, Bruno M. Pereira, Tarcisio Reis, Boris Sakakushev, Gabriele Sganga, Kjetil Søreide, Michael Sugrue, Jan Ulrych, Jean‐Louis Vincent, Pierluigi Viale, Ernest E. Moore

Bibliographic record

VenuePubMed · 2017
Typebook
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsGrading (engineering)MedicineExecutive summaryAbdominal surgeryGeneral surgeryLibrary scienceSurgeryComputer scienceBusinessEngineering

Abstract

fetched live from OpenAlex

This paper reports on the consensus conference on the management of intra-abdominal infections (IAIs) which was held on July 23, 2016, in Dublin, Ireland, as a part of the annual World Society of Emergency Surgery (WSES) meeting. This document covers all aspects of the management of IAIs. The Grading of Recommendations Assessment, Development and Evaluation recommendation is used, and this document represents the executive summary of the consensus conference findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation 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: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0170.014

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 head0.047
GPT teacher head0.284
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations109
Published2017
Admission routes1
Has abstractyes

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