Personal Exposure to PM2.5, Black Carbon and Carbon Monoxide and Their Effects on Atherosclerosis: A Cross Sectional Assessment in Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
About 40% of the world’s population are exposed to hazardous particles from biomass fuel use at households. Effects of such exposures on preclinical markers of cardiovascular diseases (CVD) are practically challenging to measure and mostly unexplored in low- and middle-income countries. GEOHealth study is examining the effect of individual level exposures to PM 2.5, black carbon (BC) and carbon monoxide (CO) on atherosclerosis among 600 biomass fuel users in rural Bangladesh.We are measuring PM 2.5 by gravimetric method using personal air samplers (RTI MicroPEM™). BC is being determined by reflectance measurement using an EEL-type smoke stain Reflectometer. CO is measured by Lasker EL-USB CO data logger. We assessed Carotid intima thickness (cIMT), a marker of atherosclerosis, using the SonoSite MicroMaxx ultrasound machine equipped with a L38e/10-5 MHz transducer. We are using the mean of the near and far walls of the maximum common carotid artery (CCA) IMT from both sides of the neck as the outcome variable. Structured questionnaire is used to record important co-variates.The primary analysis was conducted among 100 women (mean age 40±8 years), who has been using biomass fuel in traditional stoves for 20±9 years. They were all non-smoker with low (<5 µg/L) exposure to water arsenic and not known to have any CVD. Average 48 hour exposure to PM2.5, BC and CO was 124µg/m3 (SD 108), 4.8 µg/m3 (SD 2.1) and 1.2 ppm (SD 0.9) respectively. Mean cIMT is 740.9µm (SD 78). We will construct a multiple linear regression model and strength of association adjusted for important co-variates will be reported. We plan to complete data analysis on 200 samples by June 2018 and expect to present the finding at the ISEE meeting.Findings will help establish effects of HAP on atherosclerosis providing insights into magnitude, underlying mechanism and prevention strategies of the problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it