Complete Blood Count (CBC) and Multivariate Analysis as Tools for Predicting Coronavirus (COVID-19) Infectious
Bibliographic record
Abstract
The COVID-19 pandemic has affected millions worldwide in recent years. However, the epidemic's impact on the residents of the southern Libyan region has not been assessed. To investigate the spread of COVID-19 among the population, a study was conducted from March to June 2021. The study involved 146 people, 97 of whom were infected with COVID-19 and 49 were not infected. A complete blood count (CBC) and multivariate statistical analysis were used to determine the extent of the epidemic's spread in the study area. The CBC analysis used China's Tecom Science Corporation, model number TEK-5000. The results revealed that males (58.76%) were more affected than females (41.24%). The most affected age group was those under 46 (53.6%). The T-test analysis showed significant differences (p > 0.01) for each Red blood cell count (RBC), Mean corpuscular haemoglobin (MCH), Mean corpuscular haemoglobin concentration (MCHC), Red cell distribution width (RDW), Platelet count (PLT), White blood cell count (WBC), Platelet count (PLT), and granulocytes (GRA). However, the Hematocrit (HCT) was less than the significance level (P < 0.05), and there was no significant difference (P > 0.05) for Hemoglobin (HGB), Mean corpuscular volume (MCV), Lymphocyte (LYM), and Monocyte (MON) compared to the uninfected group. This study indicates that COVID-19 infection significantly affects the average values of blood tests, and changes in these values may cause complications for patients. Therefore, monitoring these changes in blood values is crucial to reducing the death rate among the infected.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".