Abstract PS9-47: A scoping review characterizing “choosing wisely” recommendations for breast cancer management
Bibliographic record
Abstract
Abstract Background: Choosing Wisely (CW)® was created by the American Board of Internal Medicine (ABIM) to promote patient-physician conversations about unnecessary medical tests and treatment. It is estimated that 20% of healthcare cost is wasted on ineffective interventions. National societies such as American Society of Clinical Oncology, Americal Society of Breast Surgeons, and American Society for Radiation Oncology have developed lists of recommendations within the Choosing Wisely initiative to to eliminate non-evidence based practices and improve patient outcomes. Similarly, other countries outside of the US have created their own national panels of experts called “CW® campaigns” which typically review recommendations submitted by that country’s oncology societies. We performed a scoping review to consolidate CW® recommendations from different groups with respect to breast cancer care. Methods: A systematic search of Medline and Embase for English language publications presenting CW® recommendations for breast cancer care practices was conducted from Jan 1, 2011 - May 11, 2020. The search was designed and peer reviewed by information specialists. We also reviewed the CW® websites of ABIM and associated international CW® campaigns. Two reviewers independently screened studies for inclusion and performed data extraction, and findings were summarized narratively. Results: Review of ABIM CW® recommendations showed 26 breast cancer-related recommendations. These pertained to: screening (n=5), radiological staging (n=2), treatment (n=15), surveillance (n=2), and miscellaneous (genetic testing and pathology; n=2). Treatment recommendations were sub-classified into surgery (n= 9), chemotherapy (n= 2), radiation therapy (n= 2), and supportive therapy (n= 2). Of 20 countries which have a CW® campaign and endorse recommendations for a range of diseases, 13 have published recommendations for breast cancer. While most international campaigns published recommendations on the same topics as the ABIM campaign, 6 campaigns developed recommendations on new topics. These included: follow-up visits (Canada), involvement of multi-disciplinary teams and imaging in palliative care setting (India) and comparison of screening imaging modalities (Portugal). There was concordance in screening, treatment, and surveillance recommendations between the CW® campaigns. Conclusion: CW® recommendations focus on reducing overutilization of investigations and treatments. Breast cancer screening and treatment were most frequently addressed by CW® recommendations. There was a high rate of consensus between international CW® recommendations with respect to breast cancer care. As health care systems globally move attention to reduce low value care, further studies are required to address adherence to these current recommendations and develop new recommendations addressing topics not currently included in the US CW campaigns. Citation Format: Hely Shah, Julian Surujballi, Arif A Awan, Brian Hutton, Angel Arnaout, Risa Shorr, Lisa Vandermeer, Meshari J Alzahrani, Mark Clemons. A scoping review characterizing “choosing wisely” recommendations for breast cancer management [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS9-47.
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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 | 0.053 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".