Prognosis of COVID-19 infection among opium users in Iran,2020: a hospital-based study
Bibliographic record
Abstract
Introduction Since Iran has the highest opioid consumption in the world and the literature surrounding the association of COVID-19 with opioid consumption is still insufficient, in this study, we aimed to present the individual, clinical, and outcome characteristics of patients with COVID-19 disease who had a history of opium use.Methods In this cross-sectional study, 1,985 patients, with a history of opium consumption, who were admitted to hospital because of COVID-19 disease were evaluated. Data were obtained from February 24, 2020 to June 21 2021, using the Medical Care Monitoring Center (MCMC) system of Shiraz University of Medical Sciences located in the province of Fars.Results The mean age of patients was 57.3 ± 17.1 years. The most common symptoms of COVID-19 disease were loss of consciousness (77.4%). 25% of patients had underlying diseases, the most common of which were cardiovascular disease (21.4%) and hypertension (21.2%). Out of 1,985 patients 251 (12.6%) died due to COVID-19. Multiple logistic regression showed that age, gender, having underlying diseases and high-resolution (HRCT) of lung are associated with mortality.Conclusion Our results showed, age 40–59 years, male gender, presence of underlying disease(s) and HRCT of the lung with finding, are correlated to the mortality of COVID-19 hospitalized patients with a history of opium consumption.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".