Lymphopenia and neutrophilia at admission predicts severity and mortality in patients with COVID-19: a meta-analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is a compelling need to identify clinical and laboratory predictors of unfavorable clinical course and death in patients with coronavirus disease (COVID-19). A trend towards low lymphocyte count and high neutrophil counts in patients with poor outcomes has been reported by earlier studies. We aim to synthesize existing data evaluating the relationship between clinical outcomes and abnormal neutrophil and lymphocyte counts at admission in COVID-19 patients. METHODS: An electronic search was carried out in PubMed, China National Knowledge Infrastructure (CNKI) and Cochrane Central Register of Controlled Trials (CENTRAL) to identify eligible studies reporting frequency data on neutrophilia and lymphopenia at admission in hospitalization in COVID-19 patients. Pooled odds ratios of clinical outcomes for each parameter were calculated using Comprehensive Meta-Analysis. RESULTS: A total of 22 studies (4,969 patients) were included in this meta-analysis. Lymphopenia at admission was found to be significantly associated with increased odd of progression to severe disease (odds ratio [OR], 4.20; 95% confidence interval [95CI%], 3.46-5.09) and death (OR, 3.71; 95%CI, 1.63-8.44). Neutrophilia at admission was also found to be significantly associated with increased odd of progression to severe disease (OR, 7.99; 95%CI, 1.77-36.14) and death (OR, 7.87; 95%CI, 1.75-35.35). Subgroup analysis revealed that COVID-19 patients with severe lymphopenia (<0.5 x10×9/L) had 12-fold increased odds of in-hospital mortality. CONCLUSION: Admission lymphopenia and neutrophilia are associated with poor outcomes in patients with COVID-19. Regular monitoring and early and even more aggressive intervention shall hence be advisable in patients with low lymphocyte and high neutrophil counts. These variables may be useful in risk stratification models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it