Incidence and Prevalence of Multiple Sclerosis in the Americas: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The incidence and prevalence of multiple sclerosis (MS) varies considerably around the world. No previous study has performed a comprehensive review examining the incidence and prevalence of MS across the Americas. The purpose of this study was to systematically review and assess the quality of studies estimating the incidence and/or prevalence of MS in North, Central and South American regions. METHODS: A comprehensive literature search was performed using MEDLINE and EMBASE from January 1985 to January 2011. Search terms included 'multiple sclerosis', 'incidence', 'prevalence' and 'epidemiology'. Only full-text articles published in English or French were included. Study quality was assessed using an assessment tool based on recognized guidelines and designed specifically for this study. RESULTS: A total of 3,925 studies were initially identified, with 31 meeting the inclusion criteria. The majority of studies examined North American regions (n = 25). Heterogeneity was high among all studies, even when stratified by country. Only half of the studies reported standardized rates, making comparisons difficult. Quality scores ranged from 3/8 to 8/8. CONCLUSION: This review highlights the gaps that still exist in the epidemiological knowledge of MS in the Americas, and the inconsistencies in methodologies and quality among the published studies. There is a need for future studies of MS prevalence and incidence to include uniform case definitions, employ comparable methods of ascertainment, report standardized results, and be performed on a national level. Other factors such as sex distribution, ethnic make-up and population lifestyle habits should also be considered.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".