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Record W3013358557 · doi:10.1186/s13017-020-00302-7

Liver trauma: WSES 2020 guidelines

2020· review· en· W3013358557 on OpenAlexaff
Federico Coccolini, Raúl Coimbra, Carlos A. Ordóñez, Yoram Kluger, Felipe Vega, Ernest E. Moore, Walt Biffl, Andrew B. Peitzman, Tal M. Hörer, Fikri M. Abu‐Zidan, Massimo Sartelli, Gustavo Pereira Fraga, Enrico Cicuttin, Luca Ansaloni, Michael W. Parra, Mauricio Millán, Nicola de’Angelis, Kenji Inaba, George Velmahos, Ron Maier, Vladimir Khokha, Boris Sakakushev, Goran Augustin, Salomone Di Saverio, Mircéa Chirica, Viktor Reva, Ari Leppäniemi, Vassil Manchev, Massimo Chiarugi, Dimitrios Damaskos, Dieter Weber, Neil Parry, Zaza Demetrashvili, Ian Civil, Lena M. Napolitano, Davide Corbella, Fausto Catena

Bibliographic record

VenueWorld Journal of Emergency Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicAbdominal Trauma and Injuries
Canadian institutionsVictoria HospitalLondon Health Sciences Centre
FundersUniversidad del ValleFundación Valle del Lili
KeywordsMedicineLiver injuryMultidisciplinary approachEmergency surgeryIntensive care medicineDamage control surgeryTrauma surgeryGeneral surgeryMedical emergencyResuscitationSurgeryOrthopedic surgeryInternal medicine

Abstract

fetched live from OpenAlex

Liver injuries represent one of the most frequent life-threatening injuries in trauma patients. In determining the optimal management strategy, the anatomic injury, the hemodynamic status, and the associated injuries should be taken into consideration. Liver trauma approach may require non-operative or operative management with the intent to restore the homeostasis and the normal physiology. The management of liver trauma should be multidisciplinary including trauma surgeons, interventional radiologists, and emergency and ICU physicians. The aim of this paper is to present the World Society of Emergency Surgery (WSES) liver trauma management guidelines.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.007

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 head0.209
GPT teacher head0.416
Teacher spread0.207 · 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
GenreReview

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

Citations320
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of Emergency SurgerySame topicAbdominal Trauma and InjuriesFrench-language works237,207