Bibliographic record
Abstract
Abstract There is growing evidence that air pollutants might affect human behavior. This study assesses the associations between air pollution concentrations and emergency department (ED) visits for abuse of psychoactive substances. 28,745 such ED visits were identified and retrieved from a health database containing diagnosed visits from five hospitals in Edmonton (Canada) over 10 years. The ED visits were analyzed as daily counts. Conditional Poisson regression models were used to estimate the associations between the number of ED visits and concentration levels of gaseous air pollutants (carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), ozone (O 3 )) and particulate matters (PM 2.5 and PM 10 , fine and coarse, respectively). Air pollutants and weather factors in the realized statistical models were lagged by the same number of days, from 0 to 5 days. The associations were estimated in the form of concentration-response functions. The results show relative risks and their 95% confidence intervals. Positive and statistically significant associations were obtained for CO for all patients (lags from 0 to 5), males (lags 1 and 3–5), and females (lag 4). For NO 2 , exposure lagged by 1 and 2 days has a positive statistically significant association for all and male patients. PM 10 shows the same type of associations lagged by 2 and 3 days. PM 2.5 (lag 2) is associated only in females. The results indicate that urban air pollution may have an impact on the abuse of psychoactive substances.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".