Factors Influencing Implementation of the Commission on Cancer’s Breast Synoptic Operative Report (Alliance A20_Pilot9)
Bibliographic record
Abstract
BACKGROUND: The technical aspects of cancer surgery have a significant impact on patient outcomes. To monitor surgical quality, in 2020, the Commission on Cancer (CoC) revised its accreditation standards for cancer surgery and introduced the synoptic operative reports (SORs). The standardization of SORs holds promise, but successful implementation requires strategies to address key implementation barriers. This study aimed to identify the barriers and facilitators to implementing breast SOR within diverse CoC-accredited programs. METHODS: In-depth semi-structured interviews were conducted with 31 health care professionals across diverse CoC-accredited sites. The study used two comprehensive implementation frameworks to guide data collection and analysis. RESULTS: Successful SOR implementation was impeded by disrupted workflows, surgeon resistance to change, low prioritization of resources, and poor flow of information despite CoC's positive reputation. Participants often lacked understanding of the requirements and timeline for breast SOR and were heavily influenced by prior experiences with templates and SOR champion relationships. The perceived lack of monetary benefits (to obtaining CoC accreditation) together with the significant information technology (IT) resource requirements tempered some of the enthusiasm. Additionally, resource constraints and the redirection of personnel during the COVID-19 pandemic were noted as hurdles. CONCLUSIONS: Surgeon behavior and workflow change, IT and personnel resources, and communication and networking strategies influenced SOR implementation. During early implementation and the implementation planning phase, the primary focus was on achieving buy-in and initiating successful roll-out rather than effective use or sustainment. These findings have implications for enhancing standardization of surgical cancer care and guidance of future strategies to optimize implementation of CoC accreditation standards.
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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 | 0.038 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".