An ‘epidemic’ of multiple sclerosis and falling infection rates? Reflecting on comparisons made and the rising multiple sclerosis incidence in Bach's 2002 <i>New England Journal of Medicine</i> figure
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We set out to revisit and comment on the evidence surrounding a popular figure published in the New England Journal of Medicine (2002), which suggests that the incidence of immune-mediated diseases, including multiple sclerosis (MS), is increased by reduced exposure to infections. METHODS: Commentary. RESULTS: We found that, to date (May 2017), this influential article has been cited >2000 times. However, on close investigation of the figure, we noticed some problems. Specifically, we observed several challenges inherent in using ecological data from disparate studies and countries to make conclusions surrounding the temporal patterns and relationships between diseases. For example, the figure depicts incidence data for MS based solely on a limited group of individuals with MS (n = 637; 455 women and 182 men) living within the region of Sassari on the island of Sardinia, known for its unique genealogy and risk of MS. However, the infectious-related data were based primarily on large population studies from the USA, with one derived from army recruits in France. CONCLUSIONS: We encourage the scientific community to apply rigorous, consistent methods in order to confirm or refute whether a strong, direct relationship does or does not exist between the incidence of MS and infectious diseases. Further, our article highlights a major knowledge gap that would benefit from a thorough review of the temporal trends related to MS incidence. Collation of this wide body of knowledge may provide a balanced understanding of this important topic and would best serve the progress of MS research.
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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 | 0.032 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".