Lower Platelet-to-Lymphocyte Ratio Was Associated with Poor Prognosis for Newborn Patients in NICU
Bibliographic record
Abstract
Background: Platelet-to-lymphocyte ratio (PLR) is reported to be related to the outcome of intensive care unit (ICU) patients. However, little is known about their associations with prognosis in newborn patients in neonatal ICU (NICU). The aim of the present study was to investigate the prognostic significance of the PLR for newborn patients in the NICU. Methods: Data on newborn patients in the NICU were extracted from the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC III) database. The initial PLR value of blood examinations within 24 h was analyzed. Spearman's correlation was used to analyze the association of PLR with the length of hospital and ICU stays. The chi-square test was used to analyze the association of PLR with mortality rate. Multivariable logistic regression was used to determine whether the PLR was an independent prognostic factor of mortality. The area under the receiver operating characteristic (ROC) curve was used to assess the predictive ability of models combining PLR with other variables. Results: In total, 5240 patients were enrolled. PLR was negatively associated with length of hospital stay and ICU stay (hospital stay: ρ = −0.416, p < 0.0001; ICU stay: ρ = −0.442, p < 0.0001). PLR was significantly correlated with hospital mortality (p < 0.0001). Lower PLR was associated with higher hospital mortality (OR = 0.85, 95% CI = 0.75−0.95, p = 0.005) and 90-day mortality (OR = 0.85, 95% CI = 0.76−0.96, p = 0.010). The prognostic predictive ability of models combining PLR with other variables for hospital mortality was good (AUC for Model 1 = 0.804, 95% CI = 0.73−0.88, p < 0.0001; AUC for Model 2 = 0.964, 95% CI = 0.95−0.98, p < 0.0001). Conclusion: PLR is a novel independent risk factor for newborn patients in the NICU.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".