The ASH‐ASPHO Choosing Wisely Campaign: 5 hematologic tests and treatments to question
Bibliographic record
Abstract
Abstract Choosing Wisely is a medical stewardship and quality‐improvement initiative led by the American Board of Internal Medicine Foundation in collaboration with leading medical societies in the United States. The American Society of Hematology (ASH) has been an active participant in the Choosing Wisely project. In 2019, ASH and the American Society of Pediatric Hematology/Oncology (ASPHO) formed a joint task force to solicit, evaluate, and select items for a pediatric‐focused Choosing Wisely list. By using an iterative process and an evidence‐based method, the ASH‐ASPHO Task Force identified 5 hematologic tests and treatments that health care providers and patients should question because they are not supported by evidence, and/or they involve risks of medical and financial costs with low likelihood of benefit. The ASH‐ASPHO Choosing Wisely recommendations are as follows: (1) avoid routine preoperative hemostatic testing in an otherwise healthy child with no previous personal or family history of bleeding, (2) avoid platelet transfusion in asymptomatic children with a platelet count 10 × 103/μL unless an invasive procedure is planned, (3) avoid thrombophilia testing in children with venous access‐associated thrombosis and no positive family history, (4) avoid packed red blood cells transfusion for asymptomatic children with iron deficiency anemia and no active bleeding, and (5) avoid routine administration of granulocyte colony‐stimulating factor for prophylaxis of children with asymptomatic autoimmune neutropenia and no history of recurrent or severe infections. We recommend that health care providers carefully consider the anticipated risks and benefits of these identified tests and treatments before performing them.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.021 | 0.013 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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".