Joint Healthcare Professional and Patient Development of Communication Tools to Improve the Standard of MS Care
Bibliographic record
Abstract
INTRODUCTION: Effective communication between patients and healthcare professionals (HCPs) is important to enhance outcomes in multiple sclerosis (MS). However, in practice, patients often report a disconnect in communication. Communication tools to aid patient-HCP communication have a long history of use in many chronic conditions. For example, symptom diaries have been shown to enhance outcomes in cancer, headache and sleep disorder management. MS in the 21st Century, a Steering Group of HCP specialists and patients with MS (PwMS), has created two communication tools designed for use by both patients and their HCPs. METHODS: The Steering Group first identified prominent issues in patient-HCP communication through group discussions and survey data. Following this, a series of workshops led to the development of two communication tools as potential solutions to these identified issues in communication. RESULTS: The two most prominent issues identified were HCP time constraints during appointments and the misalignment of patient and HCP priorities-the communication tools developed through the workshops were created to address these. The "myMS priorities" tool [see supplementary materials] is designed to maximize the use of consultation time while the "myMS commitments" tool [see supplementary materials] aims to improve patient-HCP shared decision-making. CONCLUSIONS: The MS in the 21st Century Steering Group adopted a broad, iterative and collaborative approach in the development of these tools to help ensure they would be as useful as possible to both HCPs and PwMS. These tools have been developed through shared patient-HCP expertise and are based on existing tools in other therapy areas as well as a review of the existing literature and data from MS in the 21st Century Steering Group surveys. The next steps will focus on the validation of these tools through testing them in real-world environments and clinical trials. FUNDING: Merck KGaA, Darmstadt, Germany.
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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 | 0.165 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".