Importance of social ties in dissemination of Commission on Cancer's synoptic operative report
Bibliographic record
Abstract
In 2020, the Commission on Cancer (CoC) launched templated synoptic element documentation in operative reports (SORs) as an accreditation standard to standardize and document surgical techniques for key portions of cancer operations. The study team identified multi-level factors influencing implementation of CoC's breast cancer SORs, including variations in surgeons' knowledge about the new SOR standard. One identified facilitator of SOR dissemination was social ties. To better understand mechanisms underlying social ties in disseminating breast SORs, we performed secondary analysis of key informant interviews in this study. Social ties were identified by characterizing the surgeon's relationship to that program's Cancer Liaison Physician (CLP) or surgeon belonging to a CoC affiliate organization (e.g., Cancer Research Program). The CLP serving as each program's designated physician quality leader was also the central actor receiving information directly from the CoC. We found that both the CLP's direct ties to the CoC, and indirect ties (e.g., personal ties to someone with direct ties to the CoC), facilitated early dissemination of information about SORs. Leveraging interorganizational ties and providing guidance to CLPs about how and when to communicate with providers about new standards may facilitate dissemination.
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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 | 0.032 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".