Use of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in COVID-19
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: As the pandemic of coronavirus disease 2019 (COVID-19) continues, prognostic markers are now being identified. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are easily accessible values that have been known to correlate with inflammation and prognosis in several conditions. We used the available data to identify the association of NLR and PLR with the severity of COVID-19. METHODS: A literature search using EMBASE, MEDLINE, and Google Scholar for studies reporting the use of NLR and PLR in COVID-19 published until April 28, 2020, was performed. Random effects meta-analysis was done to estimate standard mean difference (SMD) of NLR and PLR values with 95% confidence interval (CI) between severe and non-severe COVID-19 cases. RESULTS: A total of 20 studies with 3,508 patients were included. Nineteen studies reported NLR values, while five studies reported PLR values between severe and non-severe COVID-19 patients. Higher levels of NLR (SMD: 2.80, 95% CI: 2.12 - 3.48, P < 0.00001) and PLR (SMD: 1.82, 95% CI: 1.03 - 2.61, P < 0.00001)) were seen in patients with severe disease compared to non-severe disease. CONCLUSIONS: NLR and PLR can be used as independent prognostic markers of disease severity in COVID-19.
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 | 0.010 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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