Systematic review on prevalence and factors associated with breathlessness due to face masks in Asian countries
Bibliographic record
Abstract
The usage of face masks has been of abundant and daily wear to every single population steeping high when the COVID-19 pandemic transmits airborne. The masks have been said to cause uneasiness and affect the performance of one’s daily living activities. Therefore, this study aims to identify the prevalence and factors associated with breathlessness due to face masks in Asian countries. Materials and Methods: Two main journal databases were adopted for this review and the study was done based on the PRISMA flow diagram. After being reviewed for stringent inclusion and exclusion criteria, the data was retrieved and compiled. Quality assessment was done using Newcastle-Ottawa Quality Assessment Scale (NOS). Results: Initial results search accounts for a total of about 800 articles to be reviewed. After eliminating duplication of articles with inclusion and exclusion criteria, we were left with nine articles. Our study shows that there is a high prevalence of breathlessness (26-100%) upon the usage of face masks with types of masks and duration of usage as its factors. Conclusion: Further studies are needed to infer the relationship between the type and duration of face mask usage with breathlessness.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".