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Record W2993330950 · doi:10.1186/s13017-019-0274-x

Kidney and uro-trauma: WSES-AAST guidelines

2019· review· en· W2993330950 on OpenAlexaff
Federico Coccolini, Ernest E. Moore, Yoram Kluger, Walter Biffl, Ari Leppäniemi, Yosuke Matsumura, Fernando Kim, Andrew B. Peitzman, Gustavo Pereira Fraga, Massimo Sartelli, Luca Ansaloni, Goran Augustin, Andrew W. Kirkpatrick, Fikri M. Abu‐Zidan, Imitiaz Wani, Dieter Weber, Emmanouil Pikoulis, Martha Larrea, C. Arvieux, Vassil Manchev, Viktor Reva, Raúl Coimbra, Vladimir Khokha, Alain Chichom‐Mefire, Carlos A. Ordóñez, Massimo Chiarugi, Fernando Osni Machado, Boris Sakakushev, Junichi Matsumoto, Ron Maier, Isidoro Di Carlo, Fausto Catena

Bibliographic record

VenueWorld Journal of Emergency Surgery · 2019
Typereview
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsMedicineKidneyGeneral surgeryIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Renal and urogenital injuries occur in approximately 10-20% of abdominal trauma in adults and children. Optimal management should take into consideration the anatomic injury, the hemodynamic status, and the associated injuries. The management of urogenital trauma aims to restore homeostasis and normal physiology especially in pediatric patients where non-operative management is considered the gold standard. As with all traumatic conditions, the management of urogenital trauma should be multidisciplinary including urologists, interventional radiologists, and trauma surgeons, as well as emergency and ICU physicians. The aim of this paper is to present the World Society of Emergency Surgery (WSES) and the American Association for the Surgery of Trauma (AAST) kidney and urogenital 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)0.003
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 score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.251
GPT teacher head0.419
Teacher spread0.169 · 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

Citations261
Published2019
Admission routes1
Has abstractyes

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