Platelet-to-lymphocyte ratio as a prognostic biomarker for COVID-19 severity: a single center retrospective data analysis and systematic review with meta-analysis of 187 studies
Bibliographic record
Abstract
INTRODUCTION: This study aims to evaluate the prognostic value of the platelet-to-lymphocyte ratio in determining the severity and mortality of adults hospitalized for COVID-19 using retrospective data and a meta-analysis of previous studies on the platelet-to-lymphocyte ratio worldwide. MATERIAL AND METHODS: A retrospective study was conducted at the Kırdar City Hospital (Istanbul, Turkey) and included 521 COVID-19 patients. A systematic literature search of EMBASE, MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL), and Google Scholar databases was performed for relevant trials relating to the PLR ratio in COVID-19 published before April 12, 2023. RESULTS: In the retrospective part of the study, PLR values were found to predict COVID-19 severity at admission with an AUC of 0.61 (SE = 0.03; 95% CI: 0.56 to 0.65; p = 0.0003) as well as survival status in a statistically significant fashion with an AUC of 0.59 (SE = 0.03; 95% CI: 0.55 to 0.64; p = 0.0004). Results of our meta-analysis showed a significant relationship between PLR and COVID-19 severity, with a pooled standardized mean difference (SMD) of 1.34 (95% CI: 1.13 to 1.55; p < 0 .001), and that PLR was significantly lower among patients who survived compared to deceased patients (SMD = –1.32; 95% CI: 1.57 to –1.07; p < 0.001). CONCLUSIONS: PLR is a valid, readily available marker that can distinguish COVID-19 individuals with distinct progression and survival outcomes.
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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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.049 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".