Emerging Therapies for the Management of Multiple Sclerosis
Bibliographic record
Abstract
Objective To provide a comprehensive overview on the emerging treatments used for the treatment management of multiple sclerosis (MS). Data Sources PubMed, MEDLINE, Cochrane, and Toxnet databases were used to conduct all comprehensive literature searches over the time period of 1989 to 2009. Search terms such as: multiple sclerosis and oral treatment, monoclonal antibodies, hormonal therapy, and stem cell transplant were used as key word search indicators. Study Selection A total of 48 studies were reviewed and selected based on Level 1, 2, and 3 search strategies. Data Extraction Level 1 search strategies were initially aimed at evidence-based trials of large sample size (N > 100) with a randomized, double-blind, placebo-controlled design in the area of specialized interest. A level 2 search was conducted for additional trials that had many but not all of the desirable traits of evidence-based trials. In addition, a level 3 search strategy was conducted to compare key findings stated in anecdotal reports of very small (N < 15), poorly designed trials with the results of well-designed, evidence-based trials identified in level 1 and/or level 2 searches. Data Synthesis and Conclusion Despite the wide array of recent treatment advances in the field of MS, the cure still remains elusive. At present, current available treatments at best are only able to slow disease progression by reducing the incidence severity and duration of MS attacks. Recent treatment advances involving the use of newly designed orally administered drugs, monoclonal antibodies with the introduction of stem cell transplantation have revolutionized clinical outcomes for MS patients. Despite great strides made toward disease attenuation, the risks associated with the new treatments are real and have to be weighed against the projected benefits of drug treatment for a disease which has no cure.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".