American Society of Hematology 2019 guidelines for sickle cell disease: cardiopulmonary and kidney disease
Bibliographic record
Abstract
BACKGROUND: Prevention and management of end-organ disease represent major challenges facing providers of children and adults with sickle cell disease (SCD). Uncertainty and variability in the screening, diagnosis, and management of cardiopulmonary and renal complications in SCD lead to varying outcomes for affected individuals. OBJECTIVE: These evidence-based guidelines of the American Society of Hematology (ASH) are intended to support patients, clinicians, and other health care professionals in their decisions about screening, diagnosis, and management of cardiopulmonary and renal complications of SCD. METHODS: ASH formed a multidisciplinary guideline panel that included 2 patient representatives and was balanced to minimize potential bias from conflicts of interest. The Mayo Evidence-Based Practice Research Program supported the guideline development process, including performing systematic evidence reviews up to September 2017. The panel prioritized clinical questions and outcomes according to their importance for clinicians and patients. The panel used the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, including GRADE evidence-to-decision frameworks, to assess evidence and make recommendations, which were subject to public comment. RESULTS: The panel agreed on 10 recommendations for screening, diagnosis, and management of cardiopulmonary and renal complications of SCD. Recommendations related to anticoagulation duration for adults with SCD and venous thromboembolism were also developed. CONCLUSIONS: Most recommendations were conditional due to a paucity of direct, high-quality evidence for outcomes of interest. Future research was identified, including the need for prospective studies to better understand the natural history of cardiopulmonary and renal disease, their relationship to patient-important outcomes, and optimal management.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".