No Effect of Birth Weight on the Risk of Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Genetic and environmental factors have important roles in multiple sclerosis (MS) susceptibility. A clear maternal effect has been shown in several population-based studies. This parent-of-origin effect could result from factors operating during gestation. It has been shown that a low birth weight increases the risk of several adult-onset diseases. In a population-based Canadian cohort, we investigated whether there is any difference in birth weight for MS index cases compared to spousal controls. METHODS: Using the longitudinal Canadian database, we identified 6,188 MS index cases and 1,640 spousal controls with birth weight information. Additionally, data were available on 164 discordant MS twins. The birth weight was compared between index cases and controls as well as for twin pairs. RESULTS: When stratifying by sex, no significant difference in birth weight was found (average female index case birth weight = 7.23 pounds, average female control birth weight = 7.19 pounds, p = 0.48; average male index case birth weight = 7.56 pounds, average male control birth weight = 7.55 pounds, p = 0.92). Furthermore, there was no difference in birth weight between affected and unaffected twins (average affected twin weight = 5.46 pounds, average unaffected twin weight = 5.44 pounds, p =0.85). CONCLUSIONS: The maternal effect in MS aetiology does not appear to act through a route that has an influence on birth weight. As birth weight is a relatively poor marker of fetal development, other factors involved in fetal and early development need to be explored to elucidate the mechanism of the increased MS risk conferred maternally.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".