2019 sickle cell disease guidelines by the American Society of Hematology: methodology, challenges, and innovations
Bibliographic record
Abstract
The American Society of Hematology (ASH) convened 5 guideline panels to develop clinical practice recommendations addressing 5 management areas of highest importance to individuals living with sickle cell disease: pain, cerebrovascular complications, pulmonary and kidney complications, transfusion, and hematopoietic stem cell transplant. Panels were multidisciplinary and consisted of patient representatives, content experts, and methodologists. The Mayo Clinic Evidence-Based Practice Center conducted systematic reviews based on a priori selected questions. In this exposition, we describe the process used by ASH, including the GRADE approach (Grades of Recommendations, Assessment, Development and Evaluation) for rating certainty of the evidence and the GRADE Evidence to Decision Framework. We also describe several unique challenges faced by the guideline panels and the specific innovations and solutions used to address them, including a curriculum to train patients to engage in guideline development, dealing with the opioid crisis, and working with indirect and noncomparative evidence.
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 | 0.102 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".