Abstract 199: Association of the Monocyte‐to‐HDL ratio (MHR) with the diagnosis and prognosis of Acute Ischemic Stroke: A systematic review with meta‐analysis
Bibliographic record
Abstract
Objective To summarize the current evidence on the diagnostic and prognostic value of Monocyte‐to‐HDL ratio (MHR) on acute ischemic stroke (AIS) Background Recently, the MHR has been studied as a potential biomarker for assessing the prognosis of different diseases. Design/Methods We searched 5 databases (PubMed, Embase, Scopus, Web of Science and Google Scholar) until June 2024. We included observational studies that evaluated the association between MHR and AIS. A meta‐analysis using a random‐effects model to estimate pooled effects was planned for each outcome and a narrative synthesis when this was not possible. The Newcastle‐Ottawa scale was used to assess the risk of bias and GRADE criteria were used to identify the certainty of evidence. Results 11 studies (7 cohorts, 3 cross‐sectionals and 1 case‐control) were included. With high uncertainty, a meta‐analysis of 4 studies showed a significantly increased risk of AIS in patients with a higher MHR (MD: 2.14, 95%CI: 1.75 to 2.54, I2: 100%, 4 studies). Regarding AIS prognosis, two separate meta‐analysis were conducted on 30‐day mortality and functional outcome. With high uncertainty, the MHR was found to be higher in deceased patients within 30 days of AIS onset (MD: 3.22, 95%CI: ‐3.28 to 9.72, I2: 99%, 2 studies), and higher in patients with mRS score > 2 at 90 days (MD: 0.15, 95%CI: ‐0.05 to 0.34, I2: 87%, 2 studies). Additionally, all studies reported a low risk of bias. Conclusions With high uncertainty, we found that MHR is a useful tool for the diagnosis and prediction of poor outcomes in AIS. However, we need more studies, especially prospective ones, in order to improve the evidence regarding this index.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".