American Society of Hematology, ABHH, ACHO, Grupo CAHT, Grupo CLAHT, SAH, SBHH, SHU, SOCHIHEM, SOMETH, Sociedad Panamena de Hematología, Sociedad Peruana de Hematología, and SVH 2023 guidelines for diagnosis of venous thromboembolism and for its management in special populations in Latin America
Bibliographic record
Abstract
Implementation of international guidelines in Latin American settings requires additional considerations (ie, values and preferences, resources, accessibility, feasibility, and impact on health equity). The purpose of this guideline is to provide evidence-based recommendations about the diagnosis of venous thromboembolism (VTE) and its management in children and during pregnancy. We used the GRADE ADOLOPMENT method to adapt recommendations from 3 American Society of Hematology (ASH) VTE guidelines (diagnosis of VTE, VTE in pregnancy, and VTE in the pediatric population). ASH and 12 local hematology societies formed a guideline panel comprising medical professionals from 10 countries in Latin America. Panelists prioritized 10 questions about the diagnosis of VTE and 18 questions about its management in special populations that were relevant for 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. In comparison with the original guideline, there were significant changes in 2 of 10 diagnostic recommendations (changes in the diagnostic algorithms) and in 9 of 18 management recommendations (4 changed direction and 5 changed strength). This guideline ADOLOPMENT project highlighted the importance of contextualizing 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".