KADAR C-REACTIVE PROTEIN, D-DIMER, DAN LAKTAT DEHIDROGENASE SEBAGAI PREDIKTOR LUARAN COVID-19 PADA ANAK: SEBUAH KAJIAN SISTEMATIS
Bibliographic record
Abstract
Background: Coronavirus Disease (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). During a surge in COVID-19 cases, Indonesia has the highest child mortality rate due to COVID-19. Until now, there have not been many studies that explain the laboratory characteristics of COVID-19 in children. This systematic review aimed to assess the significance of laboratory findings, specifically c-reactive protein (CRP), D-dimer, and lactate dehydrogenase (LDH), to predict the severity of COVID-19 in children. Methods: A systematic review was conducted through PubMed, Scopus, Cochrane, and Google Scholars to search for studies analyzing the prognostic value of c-reactive protein (CRP), D-dimer, and lactate dehydrogenase (LDH) in children with COVID-19. Quality assessments of studies were performed using the Newcastle-Ottawa Scale. Discussion: The search yielded 11 studies with a total of 3424 subjects. C-reactive protein (CRP) levels were significantly increased in pediatric patients with severe/critical COVID-19. The concentration of CRP can reflect the severity of the disease and the magnitude of the acute inflammatory response. Moreover, children with complications had higher levels of c-reactive protein (CRP) and D-dimer. Conclusion: In conclusion, c-reactive protein (CRP) levels can be a potential biomarker to improve early identification and treatment of severe COVID-19 disease in children. Further studies on D-dimer and lactate dehydrogenase (LDH) levels as markers of COVID-19 severity are still needed to provide strong recommendations.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".