The Position of Neuromuscular Patients in Shared Decision Making. Report from the 235th ENMC Workshop: Milan, Italy, January 19-20, 2018
Bibliographic record
Abstract
In the era of patient-centered medicine, shared decision-making (SDM) - in which healthcare professionals and patients exchange information and preferences and jointly reach a decision - has emerged as the gold standard model for the provision of formal healthcare. Indeed, in many geographical settings, patients are frequently invited to participate in choices concerning the design and delivery of their medical management. From a clinical perspective, benefits of this type of patient involvement encompass, for example, enhanced treatment satisfaction, improved medical compliance, better health outcomes, and maintained or promoted quality of life. Yet, although the theory and enactment of SDM in healthcare are well-described in the literature [1-3], comparatively less attention has been devoted to contextualizing questions relating to if, when, and how to include patients in decisions within medical research. In this context, patient involvement would be expected to be potentially relevant for and applicable to a wide range of activities and processes, from the identification of research priorities and development of grant applications, to the design of patient information and consent procedures, formulation of interventions, identification and recruitment of study sample populations, feasibility of a clinical trial, identification, selection, and specification of endpoints and outcomes in clinical trials and observational studies, data collection and analysis, and dissemination of results. To this end, 45 clinicians, healthcare professionals, researchers, patients, caregivers, and representatives from regulatory authorities and pharmaceutical companies from 15 different countries met to discuss the level of involvement of patients with neuromuscular diseases, specifically in the following settings of medical research for neuromuscular diseases: i) registries and biobanks; ii) clinical trials; and iii) regulatory processes. In this report, we present summaries of the talks that were given during the workshop, as well as discussion outcomes from the three topic areas listed above.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".