Predictive performance of stress hyperglycemia ratio for poor prognosis in critically ill patients: a systematic review and dose–response meta-analysis
Bibliographic record
Abstract
Stress hyperglycemia ratio (SHR) refers to the ratio of blood glucose levels to glycated hemoglobin (HbA1c) levels upon admission. As a non-invasive and easily obtainable indicator, there is no systematic evidence-based medical evidence to support the value of SHR in predicting the poor prognosis of critically ill patients at present. PubMed, Embase, Cochrane Library, and Web of Science databases were systematically searched with a December 20, 2024 deadline. Prospective and retrospective cohort studies were included. Two investigators independently screened studies and extracted data. The Newcastle–Ottawa scale was applied to appraise the risk of bias in the studies included. Statistical analysis was executed by means of Stata 15.1 software. Study heterogeneity was assessed with the I 2 statistic. A restricted cubic spline model was leveraged for dose–response analysis. Sensitivity analyses were implemented to verify the stability of the results using the one-by-one exclusion method, and subgroup analyses were conducted with disease types. 39 studies involving 102,414 patients were included. The meta-analysis revealed that SHR exhibited a noticeable positive link with major adverse cardiovascular events (MACE), 30-day mortality, one-year mortality, cardiovascular mortality (CVM), and all-cause mortality (ACM) in critically ill patients. Dose–response analysis showed that a positive link of SHR with multiple mortality was noted in critically ill patients. SHR is an independent predictor of mortality in critically ill patients, and its dynamic monitoring could provide essential support for early clinical risk stratification and intervention strategies.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.048 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".