COVID-19 Induced Acute Respiratory Distress Syndrome; A systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction Acute Respiratory Distress Syndrome (ARDS) is a potentially fatal form of respiratory failure among COVID-19 patients. Globally, there are inconsistent findings regarding acute respiratory distress syndrome among COVID-19 patients. Therefore, the aim of this study is to estimate the pooled prevalence of acute respiratory syndrome among COVID-19 patients Methods We have accessed previous studies through an electronic web-based search strategy using PubMed, Google, Google Scholar, and Cochrane Library with a combination of search terms. The quality of each included article was assessed using the Newcastle Ottawa assessment Scale for cross-sectional studies. All statistical analyses were done using STATA version 14 Software for Windows, and meta-analysis was carried out using a random effect model. Heterogeneity was assessed using Cochrane Q statistics and I-Square (I2), and the publication bias was detected based on the graphic asymmetry of funnel plot and/or Egger’s test. Results Out of 645 studies screened, 11 studies with 2845 participants fulfilled the inclusion criteria and were included in the proportion estimation. The overall pooled prevalence of ARDS was found to be 32.2%(95% CI = 27.70%-41.73%). The heterogeneity test (I2) of the study was 97.3% with p value < 0.001. The study indicates there is a considerable variability across the studies. Subgroup analysis and meta-regression were computed to detect the effect of variation. Furthermore, the publication bias was evaluated then after the trim and fill analysis was conducted. Conclusion The pooled prevalence of COVID-19 induced acute respiratory distress syndrome was found to be high, which needs a global effort to combat its morbidity and mortality. Therefore, both the governmental and non-governmental organizations better give emphasis on COVID-19 prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".