Surgery and risk for multiple sclerosis: a systematic review and meta-analysis of case–control studies
Bibliographic record
Abstract
BACKGROUND: Although the precise etiology of multiple sclerosis is largely unknown, there is some speculation that a prior history of surgery may be associated with the subsequent risk for developing the disease. Therefore, we aimed to examine surgery as a risk factor for the diagnosis of multiple sclerosis. METHODS: We searched for observational studies that evaluated the risk for developing multiple sclerosis after surgery that occurred in childhood (≤ 20 years of age) or "premorbid" (> 20 years of age). We specifically included surgeries classified as: tonsillectomy, appendectomy, adenoidectomy, or "surgery". We performed a systematic review and meta-analyses and calculated odds ratios (OR) and their 95% confidence intervals (CIs) using a random effects model. RESULTS: We identified 33 case-control studies, involving 27,373 multiple sclerosis cases and 211,756 controls. There was a statistically significant association between tonsillectomy (OR = 1.32, 95% CI 1.08-1.61; 12 studies, I(2) = 44%) and appendectomy (OR = 1.16, 95% CI 1.01-1.34; 7 studies, I(2) = 0%) in individual's ≤ 20 years of age and the subsequent risk for developing multiple sclerosis. There was no statistically significant association between risk for multiple sclerosis and tonsillectomy occurring after age 20 (OR = 1.20, 95% CI 0.94-1.53; 9 studies, I(2) = 32%), in those with appendectomy at > 20 years (OR = 1.26, 95% CI 0.92-1.72; 5 studies, I(2) = 46%), and in those with adenoidectomy at ≤ 20 years of age (OR = 1.06, 95% CI 0.68-1.68; 3 studies, I(2) = 35%). The combined OR of 15 studies (N = 2,380) looking at "surgery" before multiple sclerosis diagnosis was not statistically significant (OR = 1.19, 95% CI 0.83-1.70; I(2) = 71%). CONCLUSIONS: We found a small but statistically significant and clinically important increased risk for developing multiple sclerosis, in those with tonsillectomy and appendectomy at ≤ 20 years of age. There was no convincing evidence to support the association of other surgeries and the risk for multiple sclerosis. Well-designed prospective etiological studies, pertaining to the risk for developing multiple sclerosis, ought to be conducted and should include the examination of various surgeries as risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".