Clinical profile and short-term outcomes of RT-PCR positive COVID-19 patients in a tertiary care hospital in Dhaka, Bangladesh
Bibliographic record
Abstract
Abstract Background: After one year since emerging from Wuhan, China, the Coronavirus disease 2019 (COVID-19) pandemic is still raging worldwide. Still, there is a dearth of original research exploring the attributes of COVID-19 patients from lower-and middle-income countries, such as Bangladesh. Based on a case series from a tertiary healthcare center, this observational study has explored the epidemiological and clinical profile of COVID-19 patients in Dhaka, Bangladesh. A total of 422 COVID-19 confirmed patients (via Reverse transcription-polymerase chain reaction test) were enrolled in this study. We have compiled patients' medical records and reported their demographic, socioeconomic, and clinical features, treatment history, health outcome, and post-discharge complications descriptively.Result: Patients were predominantly male (64%), between 35 to 49 years (28%), with at least one comorbidity (52%), and had COVID-19 symptoms for one week before hospitalization (66%). A significantly higher proportion (P<0.05) of male patients had diabetes, hypertension, and ischemic heart disease, while females had a significantly higher proportion (P<0.05) of asthma. The most common symptoms were fever (80%), cough (60%), dyspnea (41%), and sore throat (21%). Most patients received antibiotics (77%) and anticoagulant therapy (56%) and stayed in the hospital for an average of 12 days. Over 90% of patients were successfully weaned, while 3% died from COVID-19, and 41% reported complications after discharge.Conclusion: The diversity of clinical and epidemiological characteristics and health outcomes of COVID-19 patients across age groups and gender is noteworthy. Our result will inform the clinicians and epidemiologists of Bangladesh of their COVID-19 mitigation effort.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".