Identifying existing Choosing Wisely recommendations of high relevance and importance to hematology
Bibliographic record
Abstract
Choosing Wisely (CW) is a medical stewardship initiative led by the American Board of Internal Medicine Foundation in collaboration with professional medical societies in the United States. In an effort to learn from and leverage the work of others, the American Society of Hematology CW Task Force developed a method to identify and prioritize CW recommendations from other medical societies of high relevance and importance to patients with blood disorders and their physicians. All 380 CW recommendations were reviewed and assessed for relevance and importance. Relevance was assessed using the MORE(TM) relevance scale. Importance was assessed with regard to six guiding principles: harm avoidance, evidence, aggregate cost, relevance, frequency and impact. Harm avoidance was considered the most important principle. Ten highly relevant and important recommendations were identified from a variety of professional societies. Recommendations focused on decreasing unnecessary imaging, blood work, treatments and transfusions, as well as on increasing collaboration across disciplines and considering value when recommending treatments. Many CW recommendations have relevance beyond the society of origin. The methods developed by the ASH CW Task Force could be easily adapted by other Societies to identify additional CW recommendations of relevance and importance to their fields. Am. J. Hematol. 91:787-792, 2016. © 2016 Wiley Periodicals, Inc.
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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 | 0.103 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".