Controlled human exposures to wood smoke: a synthesis of the evidence
Bibliographic record
Abstract
BACKGROUND: Exposure to particulate matter (PM) from wood combustion represents a global health risk, encompassing diverse exposure sources; indoor exposures due to cooking in developing countries, ambient PM exposures from residential wood combustion in developed countries, and the predicted increasing number of wildfires due to global warming. Although physicochemical properties of the PM, as well as the exposure levels vary considerably between these sources, controlled human exposure studies may provide valuable insight to the harmful effects of wood smoke (WS) exposures in general. However, no previous review has focused specifically on controlled human exposure studies to WS. RESULTS: The 22 publications identified, resulting from 12 controlled human studies, applied a range of combustion conditions, exposure levels and durations, and exercise components in their WS exposure. A range of airway, cardiovascular and systemic endpoints were assessed, including lung function and heart rate measures, inflammation and oxidative stress. However, the possibility for drawing general conclusions was precluded by the large variation in study design, resulting in differences in physicochemical properties of WS, effective dose, as well as included endpoints and time-points for analysis. Overall, there was most consistency in reported effects for airways, while oxidative stress, systemic inflammation and cardiovascular physiology did not show any clear patterns. CONCLUSION: Based on the reviewed controlled human exposure studies, conclusions regarding effects of acute WS exposure on human health are premature. Thus, more carefully conducted human studies are needed. Future studies should pay particular attention to the applied WS exposure, to assure that both exposure levels and PM properties reflect the research question.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".