Adherence in multiple sclerosis (ADAMS): Classification, relevance, and research needs. A meeting report
Bibliographic record
Abstract
BACKGROUND: Adherence to medical interventions is a global problem. With an increasing amount of partially effective but expensive drug treatments adherence is increasingly relevant in multiple sclerosis (MS). Perceived lack of efficacy and side effects as well as neuropsychiatric factors such as forgetfulness, fatigue and depression are major determinants. However, research on adherence to behavioural interventions as part of rehabilitative interventions has only rarely been studied. METHODS: In a one-day meeting health researchers as well as patient representatives and other stakeholders discussed adherence issues in MS and developed a general draft research agenda within a focus group session. RESULTS: The focus group addressed four major areas: (1) focussing patients and their informal team; (2) studying health care professionals; (3) comparing practice across cultures; and (4) studying new adherence interventions. CONCLUSIONS: A focus on patient preferences as well as a non-judgmental discussion on adherence issues with patients should be at the core of adherence work.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".