Bibliographic record
Abstract
for the self-management of exposure and the empowerment of patients in dealing with air quality.Real-life choices to personally reduce exposure to ambient air pollution are limited, particularly in regions (such as Canada) where air quality is rather good.As shown by Ragettli and colleagues, choosing a high-versus a low-exposure route had a minor effect on exposure (9).Time spent in commute contributed 8% versus 5%, respectively, to total personal exposure to nitrogen dioxide, although traffic is its dominant local source.Given the more homogenous spatial distribution of PM 2.5 , differences between low-and high-exposure choices would likely be smaller.To echo the overstated promises of "personalized medicine" with overselling "personalized prevention" from environmental hazards is a slippery road.The large burden of diseases attributable to ambient air pollution (10, 11) will not be reduced by air quality apps, personalized tools, or empowered patients."Personalized prevention" of the air pollution burden depends on strong policy makers and governments setting science-based air quality standards and on clean air agencies who rigorously implement air management plans.The global community is very far from this goal, as only nine governments around the world adopted the PM standards proposed by the World Health Organization ( 12).Once all countries comply with these standards, the globe will be a much healthier place (13).On the basis of the findings of To and colleagues, it will also be a place with fewer cases of ACOS, although the related benefit may turn out to be smaller than expected from the threefold hazard rate.n
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".