Late Breaking Abstract - Estimating the relationship between environmental exposures and acute exacerbations of COPD
Bibliographic record
Abstract
Background: Chronic obstructive pulmonary disease (COPD) is a common and chronic lung condition characterized by sudden flare-ups known as acute exacerbations (AECOPD). Relative increases in air pollution concentration and ambient temperature may play a clinically relevant role in precipitating AECOPDs. Objectives: To estimate the association between short-term exposures to air pollution and exacerbations in patients with mild-moderate COPD, as well as the influence of temperature on this relationship. Methods: In this case-crossover study, AECOPD events were collected prospectively from COPD participants within the Canadian Cohort Obstructive Lung Disease (CanCOLD). Daily particulate matter <2.5 microns (PM2.5), nitrogen dioxide (NO2), ozone (O3) and mean temperature estimates were obtained from national databases. Hazard and control periods on Day ‘0’ (day-of-event) as well as lags (Days ‘-1’ to ‘-6’) were compared by fitting conditional logistic regression models with generalized estimating equations. All data were dichotomized into ‘Warm’ (May-Oct.) and ‘Cool’ (Nov.-Apr.) periods. Single-pollutant models, unadjusted (un.) and adjusted (adj.) for mean temperature, were fitted. Results: Consistent positive associations (P<0.05) were observed between NO2 (un. 1.11 [1.01,1.22], adj. 1.13 [1.02,1.26]) and PM2.5 (un. 1.11 [1.02,1.20], adj. 1.13 [1.03,1.24]) with AECOPDs on Lag Day -1 in the Cool period. The effects of O3 concentration on AECOPDs were equivocal. Measurements and Main Results: Exposure to ambient PM2.5 and NO2 was associated with an increased odds of AECOPD (particularly on lag Day -1 and during the Cool period), challenging the conventional understanding of the precipitants for AECOPD.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".