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Record W4311761359 · doi:10.1093/ejcts/ezac488

Joint 2022 European Society of Thoracic Surgeons and The American Association for Thoracic Surgery guidelines for the prevention of cancer-associated venous thromboembolism in thoracic surgery

2022· article· en· W4311761359 on OpenAlexaff
Yaron Shargall, Wojtek Wiercioch, Alessandro Brunelli, Sudish C. Murthy, Wayne L. Hofstetter, Jules Lin, Hui Li, Lori‐Ann Linkins, Marc Crowther, Roger Davis, Gaetano Rocco, Gian Paolo Morgano, Finn Schünemann, Giovanna Elsa Ute Muti-Schünemann, James Douketis, Holger J. Schünemann, Virginia R. Litle

Bibliographic record

VenueEuropean Journal of Cardio-Thoracic Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsImpactMcMaster University
Fundersnot available
KeywordsMedicineCardiothoracic surgeryVenous thromboembolismSurgeryCancerGeneral surgeryInternal medicineThrombosis

Abstract

fetched live from OpenAlex

BACKGROUND: Venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism, is a potentially fatal but preventable postoperative complication. Thoracic oncology patients undergoing surgical resection, often after multimodality induction therapy, represent among the highest risk groups for postoperative VTE. Currently there are no VTE prophylaxis guidelines specific to these thoracic surgery patients. Evidenced-based recommendations will help clinicians manage and mitigate risk of VTE in the postoperative period and inform best practice. OBJECTIVE: These joint evidence-based guidelines from The American Association for Thoracic Surgery and the European Society of Thoracic Surgeons aim to inform clinicians and patients in decisions about prophylaxis to prevent VTE in patients undergoing surgical resection for lung or esophageal cancer. METHODS: The American Association for Thoracic Surgery and the European Society of Thoracic Surgeons formed a multidisciplinary guideline panel that included broad membership to minimize potential bias when formulating recommendations. The McMaster University GRADE Centre supported the guideline development process, including updating or performing systematic evidence reviews. The panel prioritized clinical questions and outcomes according to their importance for clinicians and patients. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used, including GRADE Evidence-to-Decision frameworks, which were subject to public comment. RESULTS: The panel agreed on 24 recommendations focused on pharmacological and mechanical methods for prophylaxis in patients undergoing lobectomy and segmentectomy, pneumonectomy, and esophagectomy, as well as extended resections for lung cancer. CONCLUSIONS: The certainty of the supporting evidence for the majority of recommendations was judged as low or very low, largely due to a lack of direct evidence for thoracic surgery. The panel made conditional recommendations for use of parenteral anticoagulation for VTE prevention, in combination with mechanical methods, over no prophylaxis for cancer patients undergoing anatomic lung resection or esophagectomy. Other key recommendations include: conditional recommendations for using parenteral anticoagulants over direct oral anticoagulants, with use of direct oral anticoagulants suggested only in the context of clinical trials; conditional recommendation for using extended prophylaxis for 28 to 35 days over in-hospital prophylaxis only for patients at moderate or high risk of thrombosis; and conditional recommendations for VTE screening in patients undergoing pneumonectomy and esophagectomy. Future research priorities include the role of preoperative thromboprophylaxis and the role of risk stratification to guide use of extended prophylaxis. (J Thorac Cardiovasc Surg 2022;▪:1-31).

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)0.077
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0770.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.075
GPT teacher head0.357
Teacher spread0.282 · 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.

Study designObservational
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

Citations25
Published2022
Admission routes1
Has abstractyes

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