Systemic immune-inflammation index and risk of gestational diabetes and preeclampsia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background The systemic immune-inflammation index (SII) has been a marker and prognostic indicator of several diseases. However, its utility in pregnancy is unknown. Herein, we reviewed the evidence on the ability of SII to predict gestational diabetes mellitus (GDM) and preeclampsia (PE).Methods A systematic search of PubMed, Embase, Scopus and Web of Science was conducted for studies comparing SII between GDM/PE and non-GDM/non-PE groups. Studies reporting diagnostic accuracy data were also included. The last date of the search was 5 November 2024. Risk of bias was assessed using Newcastle Ottawa Scale. Random-effect meta-analysis was conducted comparing values of SII between GDM/PE and non-GDM/non-PE groups.Results Nine studies were eligible. Four studies reported data on GDM and five on PE. Most studies measured SII in the first trimester. The pooled analysis showed no statistically significant difference in the SII values between PE and non-PE groups (MD: 13.07, 95% confidence interval (CI): −117.21, 143.35, I2 = 78%). Meta-analysis of four studies comparing data of GDM and non-GDM groups showed that SII was significantly higher in GDM females (MD: 210.32, 95% CI: 57.3, 363.34, I2 = 94%). The sensitivity of SII to predict PE varied from 40 to 77.5% while specificity varied from 53.8 to 67.5%. For studies on GDM, the sensitivity and specificity values varied from 66 to 80.2% and 34.4 to 65%, respectively.Conclusions SII values are significantly higher in GDM compared to non-GDM females. However, SII values did not correlate with PE. SII may have potential in predicting GDM which needs to be explored by further studies.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.007 | 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".