The prognostic predictive value of systemic immune index and systemic inflammatory response index in nasopharyngeal carcinoma: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To study the predictive value of systemic immune index (SII) and systemic inflammatory response index (SIRI) in the prognosis of patients with nasopharyngeal carcinoma. Methods: Two researchers independently searched PubMed, Cochrane, Embase, and Web of Science databases (until March 18, 2022) for all studies on SII, SIRI, and prognosis in patients with nasopharyngeal carcinoma. Quality assessment of included studies was assessed using the Newcastle-Ottawa Scale (NOS). In addition, a bivariate mixed-effects model was used to explore predictive value. Results: A total of 9 studies that satisfied the requirements were included, involving, 3187 patients with nasopharyngeal carcinoma. The results of the meta-analysis showed that SII could be an independent predictor of OS (HR=1.78, 95%CI [1.44-2.20], Z=5.28, P<0.05), and SII could also be an independent predictor of PFS (HR=1.66, 95%CI [1.36-2.03], Z=4.94, P<0.05). In addition, SIRI could also serve as an independent predictor of OS (HR=2.88, 95%CI [1.97-4.19], Z=5.51, P<0.05). The ROC area was 0.63, the sensitivity was 0.68 (95%CI [0.55-0.78]), and the specificity was 0.55 (95%CI [0.47-0.62]), all of which indicated that SII had a certain predictive value for OS. Conclusion: SII and SIRI can be used as independent predictors to predict the prognosis and survival status of patients with nasopharyngeal carcinoma and have certain predictive accuracy. Therefore, SII and SIRI should be considered in studies that update survival risk assessment systems. Systematic Review Registration: https://www.ytyhdyy.com/, identifier PROSPERO (CRD42022319678).
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".