Late-breaking abstract: Cough aerosol: Significant basis to design innovative control strategies in tuberculosis transmission in smokers
Bibliographic record
Abstract
Introduction: Smoking progressively alters the properties of airway mucus. Altered mucus responds differently to airflow interaction when coughing and may modify bioaerosol production. Objective: Characterize the cough bioaerosol in smokers to understand its relation to the transmission of tuberculosis (TB). Method: Cough aerosol was assessed in seven long-term smokers and compared with the aerosol of 44 non-smokers. Measurement of the size and number of cough droplets was accomplished using a laser diffraction system. Results: Long-term smokers emitted up to two orders of magnitude more droplets of all sizes than non-smokers when coughing. Average sum of cough droplets emitted by non-smokers and long-term smokers Subject Droplets size N<0.5μm 0.5 1 2.5 10 >100μm Non-Smokers (44) 1.33E+07 2.39E+05 2.78E+04 3.21E+04 1.76E+03 0 Smoker 1 2.15E+09 5.03E+09 9.93E+03 1.72E+05 3.34E+03 35 Smoker 2 8.75E+07 1.80E+07 1.91E+07 2.09E+06 1.10E+04 0 Smoker 3 5.47E+07 2.23E+07 8.86E+08 1.49E+07 2.24E+05 0 Smoker 4 4.31E+09 1.4E+08 4.80E+05 1.09E+05 3.75E+05 1.52E+04 Smoker 5 3.67E+06 6.90E+06 1.48E+05 1.46E+05 1.60E+03 0 Smoker 6 4.80E+08 2.29E+07 3.84E+06 4.04E+06 1.26E+05 1.03E+04 Smoker 7 0 0 2.57E+05 3.72E+05 1.64E+04 0 #/cc/one second. Conclusions: The number of droplets emitted by smokers when coughing could help to better understand the etiological association between smoking and TB. Optimal control of bioaerosol in smokers with TB, especially those with multiple and extreme drug resistance TB, might strengthen existing core TB control strategy. Acquired knowledge would lead to the development of informed public health policies, more effective practices and/or products to optimize TB control.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".