Methodology for adaptation of the ASH Guidelines for Management of Venous Thromboembolism for the Latin American context
Bibliographic record
Abstract
BACKGROUND: From 2017 to 2020, the American Society of Hematology (ASH) collaborated with 12 hematology societies in Latin America to adapt the ASH guidelines on venous thromboembolism (VTE). OBJECTIVE: To describe the methods used to adapt the ASH guidelines on venous thromboembolism. METHODS: Each society nominated 1 individual to serve on the guideline panel. The work of the panel was facilitated by the 2 methodologists. The methods team selected 4 of the original VTE guidelines for a first round. To select the most relevant questions, a 2-step prioritization process was conducted through an on-line survey and then through in-person discussion. During an in-person meeting in Rio de Janeiro, Brazil, from 23 April through 26 April 2018, the panel developed recommendations using the ADOLOPMENT approach. Evidence about health effects from the original guidelines was reused, but important data about resource use, accessibility, feasibility, and impact in health equity were added. RESULTS: In the guideline accompanying this paper, Latin American panelists selected 17 questions from an original pool of 49. Of the 17 questions addressed, substantial changes were introduced for 5 recommendations, and remarks were added or modified for 12 recommendations. CONCLUSIONS: By using the evidence from an international guideline, a significant amount of work and time were saved; by adding regional evidence, the final recommendations were tailored to the Latin American context. This experience offers an alternative to develop guidelines relevant to local contexts through a global collaboration.
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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 | 0.100 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".