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American Society of Hematology, ABHH, ACHO, Grupo CAHT, Grupo CLAHT, SAH, SBHH, SHU, SOCHIHEM, SOMETH, Sociedad Panameña de Hematología, Sociedad Peruana de Hematología, and SVH 2022 guidelines for prevention of venous thromboembolism in surgical and medical patients and long-distance travelers in Latin America

2022· article· en· W4213452461 on OpenAlexaff
Ignacio Neumann, Ariel Izcovich, Ricardo Aguilar, Guillermo León Basantes, Patricia Casais, Cecilia Colorio, Cecilia Guillermo, P. Lázaro, Jaime Pereira, Luis Antonio Meillón‐García, Suely Meireles Rezende, Juan Carlos Serrano, Mario L. Tejerina Valle, Felipe Vera, Lorena Karzulovic, Gabriel Rada, Holger J. Schünemann

Bibliographic record

VenueBlood Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsHematologyInternal medicineMedicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Venous thromboembolism (VTE) is a common disease in Latin American settings. Implementation of international guidelines in Latin American settings requires additional considerations. OBJECTIVE: To provide evidence-based guidelines about VTE prevention for Latin American patients, clinicians, and decision makers. METHODS: We used the GRADE ADOLOPMENT method to adapt recommendations from 2 American Society of Hematology (ASH) VTE guidelines (Prevention of VTE in Surgical Patients and Prophylaxis for Medical Patients). ASH and 12 local hematology societies formed a guideline panel composed of medical professionals from 10 countries in Latin America. Panelists prioritized 20 questions relevant to the Latin American context. A knowledge synthesis team updated evidence reviews of health effects conducted for the original ASH guidelines and summarized information about factors specific to the Latin American context, that is, values and preferences, resources, accessibility, feasibility, and impact on health equity. RESULTS: The panel agreed on 21 recommendations. In comparison with the original guideline, 6 recommendations changed direction and 4 recommendations changed strength. CONCLUSIONS: This guideline ADOLOPMENT project highlighted the importance of contextualization of recommendations in other settings, based on differences in values, resources, feasibility, and health equity impact.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.333
Teacher spread0.311 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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