Physical trauma and risk of multiple sclerosis: A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: We aimed to examine physical trauma as a risk factor for the subsequent diagnosis of MS. METHODS: We searched for observational studies that evaluated the risk for developing MS after physical trauma that occurred in childhood (≤20 years) or "premorbid" (>20 years). We performed a meta-analysis using a random effects model. RESULTS: We identified 1362 individual studies, of which 36 case-control studies and 4 cohort studies met the inclusion criteria for the review. In high quality case-control studies, there were statistically significant associations between those sustaining head trauma in childhood (OR=1.27; 95% CI, 1.12-1.44; p<0.001), premorbid head trauma (OR=1.40; 95% CI, 1.08-1.81; p=0.01), and other traumas during childhood (OR=2.31; 95% CI, 1.06-5.04; p=0.04) and the risk of being diagnosed with MS. In lesser quality studies, there was a statistical association between "other traumas" premorbid and spinal injury premorbid. No association was found between spinal injury during childhood, or fractures and burns at any age and the diagnosis of MS. The pooled OR of four cohort studies looking at premorbid head trauma was not statistically significant. CONCLUSIONS: The result of the meta-analyses of high quality case-control studies suggests a statistically significant association between premorbid head trauma and the risk for developing MS. However, cohort studies did not. Future prospective studies that define trauma based on validated instruments, and include frequency of traumas per study participant, are needed.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".