The ASH Choosing Wisely® campaign: five hematologic tests and treatments to question
Bibliographic record
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 ASH is an active participant in the Choosing Wisely® project. Using an iterative process and an evidence-based method, ASH has identified 5 tests and treatments that in some circumstances are not well supported by evidence and which in certain cases involve a risk of adverse events and financial costs with low likelihood of benefit. The ASH Choosing Wisely® recommendations focus on avoiding liberal RBC transfusion, avoiding thrombophilia testing in adults in the setting of transient major thrombosis risk factors, avoiding inferior vena cava filter usage except in specified circumstances, avoiding the use of plasma or prothrombin complex concentrate in the nonemergent reversal of vitamin K antagonists, and limiting routine computed tomography surveillance after curative-intent treatment of non-Hodgkin lymphoma. We recommend that clinicians carefully consider anticipated benefits of the identified tests and treatments before performing them.
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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.025 | 0.015 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".