Multiple sclerosis risk among anti-tumor necrosis factor alpha users: A methodological review of observational studies based on real-world data
Bibliographic record
Abstract
Epidemiologic studies on the risk of multiple sclerosis (MS) or demyelinating events associated with anti-tumor necrosis factor alpha (TNFα) use among patients with rheumatic diseases or inflammatory bowel diseases have shown conflicting results. Causal directed acyclic graphs (cDAGs) are useful tools for understanding the differing results and identifying the structure of potential contributing biases. Most of the available literature on cDAGs uses language that might be unfamiliar to clinicians. This article demonstrates how cDAGs can be used to determine whether there is a confounder, a mediator or collider-stratification bias and when to adjust for them appropriately. We also use a case study to show how to control for potential biases by drawing a cDAG depicting anti-TNFα use and its potential to contribute to MS onset. Finally, we describe potential biases that might have led to contradictory results in previous studies that examined the effect of anti-TNFα and MS, including confounding, confounding by contraindication, and bias due to measurement error. Clinicians and researchers should be cognizant of confounding, confounding by contraindication, and bias due to measurement error when reviewing future studies on the risk of MS or demyelinating events associated with anti-TNFα use. cDAGs are a useful tool for selecting variables and identifying the structure of different biases that can affect the validity of observational studies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.048 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".