American Society of Hematology 2020 guidelines for sickle cell disease: management of acute and chronic pain
Bibliographic record
Abstract
BACKGROUND: The management of acute and chronic pain for individuals living with sickle cell disease (SCD) is a clinical challenge. This reflects the paucity of clinical SCD pain research and limited understanding of the complex biological differences between acute and chronic pain. These issues collectively create barriers to effective, targeted interventions. Optimal pain management requires interdisciplinary care. OBJECTIVE: These evidence-based guidelines developed by the American Society of Hematology (ASH) are intended to support patients, clinicians, and other health care professionals in pain management decisions for children and adults with SCD. METHODS: ASH formed a multidisciplinary panel, including 2 patient representatives, that was thoroughly vetted to minimize bias from conflicts of interest. The Mayo Evidence-Based Practice Research Program supported the guideline development process, including updating or performing systematic reviews. Clinical questions and outcomes were prioritized according to importance for clinicians and patients. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used, including GRADE evidence-to-decision frameworks, to assess evidence and make recommendations, which were subject to public comment. RESULTS: The panel reached consensus on 18 recommendations specific to acute and chronic pain. The recommendations reflect a broad pain management approach, encompassing pharmacological and nonpharmacological interventions and analgesic delivery. CONCLUSIONS: Because of low-certainty evidence and closely balanced benefits and harms, most recommendations are conditional. Patient preferences should drive clinical decisions. Policymaking, including that by payers, will require substantial debate and input from stakeholders. Randomized controlled trials and comparative-effectiveness studies are needed for chronic opioid therapy, nonopioid therapies, and nonpharmacological interventions.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".