040 Creating diverse approaches for shared decision- making (SDM) in atopic dermatitis treatment choices
Bibliographic record
Abstract
Introduction Atopic Dermatitis (AD) is a chronic skin condition influenced by genetics, immunological issues, and environmental factors. Choosing the right treatment is crucial. The Shared Decision-Making for AD aims to align treatment plans with patient expectations and enhance communication between healthcare providers and patients. Methods We established a collaborative SDM team for the treatment of AD. The team includes attending physicians, resident physicians, nurses, pharmacists, quality management, and information engineers. Adopting the Ottawa Decision Support Framework, team members underwent educational training. The SDM model is based on a structured information form, comprising two main elements: 1. PDA: Content includes evidence-based treatment options (such as phototherapy, immunomodulators, biologics, etc.). Patients make choices based on personal preferences using a 5-point scale, and the system automatically scores the preferences. Preference choice scores exceeding 24 indicate a preference for immunomodulators/biologics, while scores below 24 indicate a preference for phototherapy. 2. Outcome assessments include 1.SMM-9 options and 2.Preparation for Decision Making Scale. Results PDA were developed in 2019, and by 2023, 53 patients utilized the system, with 39 completing the PDA and 37 completing the outcome assessment. Satisfaction scores were all above 81. The system is also utilized by five medical institutions. Discussion To enhance utilization, a push notification feature was added to the system in 2021, providing QR codes for outpatient patients to read the SDM electronic forms multiple times. Simultaneously, an immediate intelligent dashboard system was established, seamlessly integrating the SDM. Conclusion(s) The interdisciplinary team successfully implemented Shared Decision-Making (SDM) for atopic dermatitis patients, regularly updating it based on the latest evidence-based practices. Utilizing technology such as Redcap and a real-time dashboard improves communication between physicians and patients. Integrated process records assist in informed treatment decisions, maximizing the benefits of SDM.
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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 | 0.062 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".