Relationship Between TNF-α and the Risk of Cerebral Palsy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective We performed a meta-analysis to investigate the relationship between blood tumor necrosis factor-alpha (TNF-α) levels and the risk of cerebral palsy (CP) in children. Methods PubMed, Web of Science, Cochrane Library and Ovid databases were searched from the date of database inception to 26 April 2022. Data were extracted and pooled from observational studies related to TNF-α and the risk of CP in children. Quality was assessed using the Newcastle-Ottawa Scale. We used the inverse variance method with a random-effects model to estimate the odds ratios with 95% confidence intervals (CIs), and stratified analyses and sensitivity analysis were utilized to analyse heterogeneity. Results Nine studies with 1,117 cases and 3,563 controls were included in our meta-analysis. The quality of the literature was good, and no publication bias was noted. According to the random-effects model, blood TNF-α levels were associated with the risk of CP (OR 1.82; 95% CI, 1.25–2.66) in a heterogeneous set of studies ( I 2 = 81.2%, p = 0.000). Conclusion Our findings indicate that elevated TNF-α levels in the blood are associated with an increased risk of CP. The association of TNF-α with CP requires further investigation.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".