Prevalence, symptom and severity of COVID 19 among permanent residents of Dhaka City
Bibliographic record
Abstract
A study was done on 385 people who survived from COVID 19 to assess the prevalence, symptom, and severity of COVID 19 of permanent residents of Dhaka city, Bangladesh during the second wave of corona manifestation. Data were collected purposively from a government and a private hospital, and general people taking treatment from home. A significant number of respondents took treatment from the Hospital during 2nd wave of COVID 19. Two-third of participants endured moderate (67.5%) type of suffering followed by mild (18.7%) and severe (13.8%) type of suffering. Most of the participants were married (88.8%) and female (51.2%). There was no significant difference between females and males suffering and the risk and severity of COVID 19 (p=694). Most of the participants (70%) had comorbidity. Time to recover from symptoms had significant relation with symptom patterns. One-third of the respondents (33%) required 4-7 days to recover from suffering. A little higher than a quarter (27.8%) recovered within 8 to 14 days and more than a quarter 105 (27.3%) recovered by 8-12 days respectively. Most of the respondents had a fever, cough, body ache and fatigue, sore throat, and breathing difficulty. Only (7.3%) had diarrhea (3.9%) and smell loss 13 (3.4%). People of permanent residence of Dhaka city suffered from COVID 19 irrespective of sex, education, professional status. They had comorbidity, required 8-14 days of hospitalization, and endured the moderate type of suffering of COVID-19.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".