Serum C‐reactive protein greater than 75 mg/dL as an early available laboratory predictor of severe COVID‐19: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Severe COVID-19 management is still challenging. Having a laboratory factor to predict the severity of a patient's condition can be very useful in how to approach each patient. There have been studies concentrating on the correlation between serum C-reactive protein (CRP) level and COVID-19 severity but we aim to reach a threshold for CRP in disease severity determination. METHODS: We conducted a thorough search on PubMed, Web of Science and Google Scholar databases from early 2019 to October 2021, and 323 studies were assessed for eligibility in three phases. We used the Newcastle-Ottawa Scale to examine the validity of the studies. The t-test was applied for the CRP level cutoffs. RESULTS: Eventually, 11 articles and 1615 patients were included in this systematic review. Our analysis evaluated combined mean, median, and standard deviation of severe patients' CRP to be respectively 73.37, 53.80, and 47.936. Based on the combined mean, 75 mg/dL was suggested as an initial threshold for baseline CRP in hospitalized patients for developing severe conditions. CONCLUSION: This study recommends that COVID-19 patients with on-admission serum CRP levels of 75 mg/dL and more are likely associated with severe conditions. Thus, anti-inflammatory agents and further following may be helpful in these patients.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".