The North American Registry for Care and Research in Multiple Sclerosis (NARCRMS)
Bibliographic record
Abstract
Although many regional multiple sclerosis (MS) databases existed in the United States and Canada, there was no single clinician-derived registry that examined this disease as a group across the North American continent. This distinction is important because information that results from such a database can potentially give perspectives about MS that cannot be derived from any single regional registry. A partnership was forged between the pharmaceutical industry and the Consortium of Multiple Sclerosis Centers (CMSC) to create a registry of patients with MS from Canada and the United States, including Puerto Rico. Case report forms were created to collect physician-derived information, and the Patient-Reported Outcomes Measurement Information System (PROMIS) was selected to capture patient-reported outcomes. As of November 2021, 754 of 1000 patients have been enrolled. Completion of recruitment is expected by the end of 2021. Twenty-five centers are participating, with an expected total of 30, including five centers from Canada. Clinical status, health economic outcomes, magnetic resonance images, and, soon, biomarkers relevant to understanding relapses and progression are collected. The short-term goal is to understand and better treat MS disease progression, and the long-term goal is its prevention. The North American Registry for Care and Research in Multiple Sclerosis (NARCRMS) is one of few clinician/patient-generated registries that examines MS across North America, including Puerto Rico. Information derived from the natural history studies should help physicians, the pharmaceutical industry, and regulatory bodies understand MS better and improve quality of life for patients with MS worldwide.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".