Supplementary Material for: Relationship between peripheral blood inflammatory factors and prognosis of subarachnoid hemorrhage: A meta-analysis
Bibliographic record
Abstract
Background: Subarachnoid hemorrhage (SAH) is a severe cerebrovascular event with high mortality and disability rate. Neuroinflammation is involved in the brain injury after SAH, but the exact association between SAH progression and peripheral blood inflammatory factors is unknown. Therefore, to determine the relationship between inflammatory factors and the prognosis of SAH, we performed a meta-analysis. Method: A systematic literature review was conducted in PubMed, EMBASE, and Cochrane Library. Studies comparing the relationship between inflammatory factors (C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-10 (IL-10), and tumor necrosis factor (TNF-α) and prognosis of SAH were included in the study. A random-effects meta-analysis was conducted based on mRS, GOS, and the occurrence of CVS, DCI, and DINDs. Sensitivity analysis was performed using the leave-one-out method. The New-castle–Ottawa Scale (NOS) for case-control studies was used to assess the quality of included studies. And for continuous variables, we calculated the mean difference (MD) with a 95% confidence interval (CI). Results: 1469 patients from 18 case-control studies met the inclusion criteria. The results found that patients in the good outcome group had significantly lower CRP levels than those in the poor outcome group (SMD: -1.15, 95% CI: -1.64– -0.66, p < 0.00001, I2 = 87%), and peripheral IL-6 levels were significantly lower in SAH patients with the good functional outcome than those with the poor functional outcome (SMD: -0.99, 95% CI: -1.48– -0.51, p < 0.0001, I2 = 88%). As for IL-10 (SMD: -0.28, 95% CI: -0.97– 0.42, p =0.43, I2 = 88%) and TNF-α (SMD: -0.40, 95% CI: -0.98– 0.19, p =0.18, I2 = 79%), due to the small number of studies, heterogeneity, and uncontrollable factors, robust conclusions cannot be drawn. Conclusion: SAH patients with good prognoses have significantly lower peripheral CRP and IL-6 levels. In addition, due to the small number of studies, heterogeneity, and uncontrollable factors, robust conclusions cannot be drawn for IL-10 and TNF-α. More high-quality studies are needed in the future to provide more specific recommendations for the clinical practice of inflammatory factors.
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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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.646 | 0.035 |
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".