ASCP’s 2021 Choosing Wisely® Recommendations: A Proud Accomplishment
Bibliographic record
Abstract
Choosing Wisely® (CW) is a campaign to engage physicians and patients in conversations about unnecessary tests, treatments, and procedures. The campaign began in the United States in 2012 and in Canada in 2014, and now many countries around the world are adapting the campaign and implementing it. Currently, approximately 80 societies in the United States have published CW recommendations. Each recommendation is supported by clinical guidelines (when necessary), evidence-based ratinale, including information about when these tests or procedures may be appropriate. A deprescribing task force led by Chair Beier was created by ASCP in November 2018 after several conversations between ASCP leadership (notably, President J. Hirshfield) and Beier. Task force members comprise pharmacists practicing in academia, community, and long-term care settings. The chair also invited pharmacists from international countries (Canada and Australia) where deprescribing initiatives have a strong focus and scientific literature base. One of the primary goals for Chair Beier was to add ASCP's voice to the ABIM CW Campaign. Because ASCP is a membership association that represents pharmacists, health care professionals, and students serving the unique medication needs of older patients, by adding its name to the list of supporting partners, the organization makes a compelling argument to address deprescribing initiatives, tools, scientific literature, and resources to assist in initiating deprescribing conversations and their subsequent implementation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".