Systemic immune-inflammation index as a potential biomarker for predicting acute pulmonary embolism: A systematic review
Bibliographic record
Abstract
Abstract Background Acute pulmonary embolism (APE) is a life-threatening condition with a high mortality rate. The pathophysiology involves various complex processes. The systemic immune-inflammatory index (SII) is a well-known biomarker that reflects the intricate balance between pro-inflammatory and anti-inflammatory immune components. In this systematic review, we aim to determine the significance of SII as a potential biomarker for APE. Method We utilized PubMed, ProQuest, EBSCOHost, and Google Scholar to search for articles. We assessed bias risk using the Newcastle Ottawa Scale (NOS). The outcomes we examined included in-hospital and long-term mortality, the severity of APE, and the sensitivity and specificity of the SII in predicting APE. Results: Four studies, involving 2,038 patients, were included for analysis. These studies discuss the use of SII in predicting APE severity, APE mortality, high-risk APE, and the occurrence of APE. SII demonstrates significant results in predicting each of these variables. Furthermore, each study establishes different SII cut-off values. Specifically, a cut-off of 1161 predicts massive APE events with a sensitivity of 91% and a specificity of 90%. A cut-off of >1235.35 differentiates high-risk APE with a sensitivity of 87.32% and a specificity of 68.85%. A cut-off of >1111x10 9 predicts overall mortality with a sensitivity of 72% and a specificity of 51%. Finally, a cut-off at 1839.91 predicts APE events with a sensitivity of 75.8% and a specificity of 61.9%. Conclusion The SII can be employed as a potential new biomarker to predict outcomes in APE patients, particularly the occurrence, severity, and mortality of APE.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".