Conjugal multiple sclerosis: Population‐based prevalence and recurrence risks in offspring
Bibliographic record
Abstract
From a population-based sample of 15,504 patients attending Canadian multiple sclerosis (MS) clinics, we have determined the frequency of conjugal MS and have estimated the recurrence risk in offspring of such matings. Twenty-three MS cases were found among 13,550 spouses of study probands for a crude conjugal rate of 0.17% (95% CI of 0.10%–0.24%). Despite ascertainment bias that expectedly inflates this number, this is a frequency intermediate between the point prevalence (0.1%) and lifetime risk (0.2%) for the general population and close to an order of magnitude less than reported for half siblings reared apart (1.06%) from the same population. Six of the 49 offspring of conjugal pairs also had MS, and age conversion gives a rate similar to the concordance rate for Canadian monozygotic twins. However, this correction may not be appropriate in this special case. Despite an ascertainment bias in favor of recognizing affected spouses and a large population sample, the common environment in adulthood shared by spousal pairs could not be shown to increase the risk of conjugal MS. Although the high recurrence rate in offspring is similarly subject to an upward bias, the low risk for MS spouses and the high risk for offspring support other data indicating that familial risk is genetically determined. Furthermore, these results imply that susceptibility alleles are shared by unrelated individuals with the disease. Ann Neurol 2000;48:927–931
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".