Development and Pilot Testing of Decision Aid for Shared Decision Making in Barrett’s Esophagus With Low-Grade Dysplasia
Bibliographic record
Abstract
GOALS: To develop an encounter decision aid [Barrett's esophagus Choice (BE-Choice)] for patients and clinicians to engage in shared decision making (SDM) for management of BE with low-grade dysplasia (BE-LGD) and assess its impact on patient-important outcomes. BACKGROUND: Currently, there are 2 strategies for management of BE-LGD-endoscopic surveillance and ablation. SDM can help patients decide on their preferred management option. STUDY: Phase-I: Patients and clinicians were engaged in a user-centered design approach to develop BE-Choice. Phase-I included review of evidence on BE-LGD management, observation of usual care (UC), creation, field-testing, and iterative development of BE-Choice in clinical settings. Phase-II: Impact of BE-Choice on patient-important outcomes (patient knowledge, decisional conflict, and patient involvement in decision making) was assessed using a controlled before-after study design (UC vs. BE-Choice). RESULTS: Phase-I: Initial prototype was designed with observation of 8 clinical encounters. With field-testing, 3 successive iterations were made before finalizing BE-Choice. BE-Choice was paper based and fulfilled the qualifying criteria of International patient decision aid standards. Phase II: 29 patients were enrolled, 8 to UC and 21 to BE-Choice. Compared with UC, use of BE-Choice improved patient knowledge (90.4% vs. 70.5%; P=0.03), decisional comfort (89.6 vs. 71.9; P=0.01), and patient involvement (OPTION score: 27.1 vs. 19.2; P=0.01). CONCLUSIONS: BE-Choice is a feasible and effective decision aid to promote SDM in the management of BE-LGD. On pilot testing, BE-Choice had promising impact on patient-important outcomes. A larger multicenter trial is needed to confirm our results and promote widespread use of BE-Choice.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".