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Record W4403523640 · doi:10.1186/s13017-024-00559-2

Surgical stabilization of rib fractures (SSRF): the WSES and CWIS position paper

2024· review· en· W4403523640 on OpenAlexaff
Giacomo Sermonesi, Riccardo Bertelli, Fredric M. Pieracci, Zsolt J. Balogh, Raúl Coimbra, Joseph M. Galante, Andreas Hecker, Dieter Weber, Zachary M. Bauman, Susan Kartiko, Bhavik Patel, SarahAnn Whitbeck, Thomas W. White, Kevin N. Harrell, Daniele Perrina, Alessia Rampini, Brian Tian, Francesco Amico, Solomon Gurmu Beka, Luigi Bonavina, Marco Ceresoli, Lorenzo Cobianchi, Federico Coccolini, Yunfeng Cui, Francesca Dal Mas, Belinda De Simone, Isidoro Di Carlo, Salomone Di Saverio, Agron Dogjani, Andreas Fette, Gustavo Pereira Fraga, Carlos Augusto Gomes, Jim Khan, Andrew W. Kirkpatrick, Vitor Favali Kruger, Ari Leppäniemi, Andrey Litvin, Andrea Mingoli, David Costa Navarro, Eliseo Passera, Michele Pisano, Mauro Podda, Emanuele Russo, Boris Sakakushev, Domenico Pietro Santonastaso, Massimo Sartelli, Vishal G. Shelat, Edward Tan, Imtiaz Wani, Fikri M. Abu‐Zidan, Walter L. Biffl, Ian Civil, Rifat Latifi, İngo Marzi, Edoardo Picetti, Manos Pikoulis, Vanni Agnoletti, Francesca Bravi, Carlo Vallicelli, Luca Ansaloni, Ernest E. Moore, Fausto Catena

Bibliographic record

VenueWorld Journal of Emergency Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicTrauma Management and Diagnosis
Canadian institutionsFoothills Medical CentreUniversity of Calgary
FundersFaculty of Medical and Health Sciences, University of AucklandCollege of Medicine and Health Sciences, United Arab Emirates UniversityRadboud Universitair Medisch CentrumUniversità degli Studi di CagliariUnited Arab Emirates UniversityNational and Kapodistrian University of AthensRadboud Universiteit
KeywordsMedicinePosition (finance)Position paperRandomized controlled trialSurgeryGeneral surgeryMedical physics

Abstract

fetched live from OpenAlex

BACKGROUND: Rib fractures are one of the most common traumatic injuries and may result in significant morbidity and mortality. Despite growing evidence, technological advances and increasing acceptance, surgical stabilization of rib fractures (SSRF) remains not uniformly considered in trauma centers. Indications, contraindications, appropriate timing, surgical approaches and utilized implants are part of an ongoing debate. The present position paper, which is endorsed by the World Society of Emergency Surgery (WSES), and supported by the Chest Wall Injury Society, aims to provide a review of the literature investigating the use of SSRF in rib fracture management to develop graded position statements, providing an updated guide and reference for SSRF. METHODS: This position paper was developed according to the WSES methodology. A steering committee performed the literature review and drafted the position paper. An international panel of experts then critically revised the manuscript and discussed it in detail, to develop a consensus on the position statements. RESULTS: A total of 287 studies (systematic reviews, randomized clinical trial, prospective and retrospective comparative studies, case series, original articles) have been selected from an initial pool of 9928 studies. Thirty-nine graded position statements were put forward to address eight crucial aspects of SSRF: surgical indications, contraindications, optimal timing of surgery, preoperative imaging evaluation, rib fracture sites for surgical fixation, management of concurrent thoracic injuries, surgical approach, stabilization methods and material selection. CONCLUSION: This consensus document addresses the key focus questions on surgical treatment of rib fractures. The expert recommendations clarify current evidences on SSRF indications, timing, operative planning, approaches and techniques, with the aim to guide clinicians in optimizing the management of rib fractures, to improve patient outcomes and direct future research.

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)0.072
metaresearch head score (Gemma)0.099
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: Review · Consensus signal: Review
Teacher disagreement score0.072
Threshold uncertainty score0.383

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0720.099
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0090.005
Science and technology studies0.0020.003
Scholarly communication0.0070.006
Open science0.0050.007
Research integrity0.0090.008
Insufficient payload (model declined to judge)0.0050.004

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 head0.070
GPT teacher head0.382
Teacher spread0.312 · 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

Citations52
Published2024
Admission routes1
Has abstractyes

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Same venueWorld Journal of Emergency SurgerySame topicTrauma Management and DiagnosisFrench-language works237,207