Treatment Optimization in Multiple Sclerosis
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 treatment of multiple sclerosis has finally become possible with the advent of the current disease-modifying therapies (DMTs) that have had a significant impact on those living with this disease. Though demonstrating clear efficacy on a number of short-term outcome measures, unfortunately, these agents are not "cures" and many patients with multiple sclerosis continue to experience disease activity in spite of treatment. Clinicians are becoming more comfortable initiating therapy with DMTs, but it is now important to focus attention on monitoring the results of the chosen therapy and deciding whether or not a patient is responding well to treatment. At present, however, clinicians lack criteria for defining optimal versus suboptimal responses to DMTs as well as evidence-based guidelines on how to improve treatment outcomes. Using a recently published model as a framework, The Canadian Multiple Sclerosis Working Group developed practical recommendations on how neurologists can assess the status of patients on DMTs and decide when it may be necessary to modify treatment in order to optimize outcomes. The Canadian Multiple Sclerosis Working Group's recommendations are based on monitoring relapses, neurological progression and MRI activity. Other possible causes of suboptimal treatment responses or treatment failure are also considered.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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