Methodology for the American Society of Hematology VTE guidelines: current best practice, innovations, and experiences
Bibliographic record
Abstract
BACKGROUND: Methods for the development of clinical guidelines have advanced dramatically over the past 2 decades to strive for trustworthiness, transparency, user-friendliness, and rigor. The American Society of Hematology (ASH) guidelines on venous thromboembolism (VTE) have followed these advances, together with application of methodological innovations. OBJECTIVE: In this article, we describe methods and methodological innovations as a model to inform future guideline enterprises by ASH and others to achieve guideline standards. Methodological innovations introduced in the development of the guidelines aim to address current challenges in guideline development. METHODS: We followed ASH policy for guideline development, which is based on the Guideline International Network (GIN)-McMaster Guideline Development Checklist and current best practices. Central coordination, specialist working groups, and expert panels were established for the development of 10 VTE guidelines. Methodological guidance resources were developed to guide the process across guidelines panels. A methods advisory group guided the development and implementation of methodological innovations to address emerging challenges and needs. RESULTS: The complete set of VTE guidelines will include >250 recommendations. Methodological innovations include the use of health-outcome descriptors, online voting with guideline development software, modeling of pathways for diagnostic questions, application of expert evidence, and a template manuscript for publication of ASH guidelines. These methods advance guideline development standards and have already informed other ASH guideline projects. CONCLUSIONS: The development of the ASH VTE guidelines followed rigorous methods and introduced methodological innovations during guideline development, striving for the highest possible level of trustworthiness, transparency, user-friendliness, and rigor.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".