Long-Term Treatment Optimization in Individuals with Multiple Sclerosis Using Disease-Modifying Therapies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The introduction of disease-modifying therapies (DMTs) for multiple sclerosis (MS) over the last 7 years has had a significant effect on the management of those living with this disease. Initially, the focus of improving treatment outcomes was on ensuring adherence to therapy by managing drug-related adverse events. However, treatment adherence is only one facet of ensuring optimal health outcomes for patients using DMTs. Therefore, a group of 80 nurses from Canada and the United States (The North American MS Nurses' Treatment Optimization Group) developed an evidence-based nursing approach to address the various factors involved in obtaining optimal patient outcomes. The goal of this nursing approach is to ensure the best possible clinical, subclinical, psychosocial, and quality-of-life outcomes for patients with MS using DMTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it