Clinical and Biochemical Characteristics of COVID-19 in a Primary Care Center in the South Batinah Region of Oman
Bibliographic record
Abstract
Objectives The main purpose of this study was to identify the clinical and biochemical profile of coronavirus disease 2019 (COVID-19) and sex differences in Rustaq Polyclinic in South Batinah Governorate. Methods This study is a retrospective chart review of COVID-19 patients diagnosed in Rustaq Polyclinic in the South Batinah region of Oman. The medical charts of 150 patients from November 10, 2020, to November 24, 2021, at Rustaq Polyclinic were retrospectively reviewed, and clinical and laboratory parameters were extracted. Information regarding the patients' demographics, risk factors, clinical symptoms or signs, and laboratory findings on admission were obtained. Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) software version 24.0 (IBM Corp., Armonk, NY, USA). Results A total of 150 study participant data were added to the analysis. Of those, 97 (64.7%) were male and 53 (35.3%) were female, with a median age of 32 and 39 among males and females, respectively. Male and female age (p=0.017) and marital status (p=0.003) were significantly different. No significant difference (p≥0.05) was observed between male and female study participants regarding their history of travel, history of contact with COVID-19-positive patients, diabetes, hypertension, stroke, and home management. Similarly, no significant difference (p≥0.05) was observed between males and females regarding their clinical presentations. Conclusion Many symptomatic patients have shown influenza-like symptoms such as fever, respiratory symptoms such as cough, sore throat, headache, myalgia, and loss of smell. The spectrum of symptomatic infection ranges from mild to severe, and most infections are not severe. It is imperative to equip ourselves with adequate understanding and protective measures such as vaccination and diverse treatment modalities against the constantly changing nature of novel coronaviruses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".