P615: The genetics update: Protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver updated genomic results
Bibliographic record
Abstract
practice as they were unsure of their role in testing for and managing Lp(a), whereas CSs endorsed Lp(a) as not only acceptable and appropriate but already adopted as part of current practice.Regarding severe hypercholesterolemia in the absence of a known genetic cause, all providers reported discussing multifactorial and familial risk as acceptable and appropriate.GCs from both institutions were broadly supportive of a genomically-informed ASCVD risk tool.At one system, PCPs and CSs found this tool to be acceptable and supported the appropriateness of adopting it if it was validated.At the other system, most PCPs and CSs thought the tool would be acceptable and appropriate to implement, but there was concern about adoption due to practical considerations such as lack of personnel, concerns of clinician responsibility, or lack of time to implement.Conclusion: Clinicians from two different settings (rural and urban; each serving distinct, yet diverse, patient populations) were recruited to explore the implementation climate and readiness for adopting genomically-informed care for severe hypercholesterolemia. Setting alone was not found to drive differences between the two sites.Rather, clinicians at both sites widely agreed on the acceptability, appropriateness, and adoption of genetic testing and patient management in all scenarios.Differences between sites arose when discussing adoption of the genomically-informed ASCVD risk stratification tool, with PCPs and CSs at one site sharing concerns about readiness to adopt such a tool in their clinical context.Future research is needed to explore barriers to adoption of a genomically-informed ASCVD risk tool, to guide implementation of such a tool and improve management of individuals at increased risk.
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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 | 0.042 | 0.061 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.105 | 0.024 |
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".