Air Quality Health Index in primary care: A feasibility study
Bibliographic record
Abstract
Rationale: Exposure to poor air quality is associated with increased morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD), asthma and heart failure. A number of countries, including Canada, report utilization of the Air Quality Health Index (AQHI) and associated health messages tailored to different AQHI categories for the public and at-risk populations to reduce exposure, adjust physical activity and optimize clinical management. Studies indicate AQHI advisories may not adequately reach or inform at-risk populations.Objectives: The objectives of this study were to design a text alert system and evaluate the feasibility of delivering AQHI forecast alerts to participants when AQHI readings exceeded low health risk. Secondary and tertiary objectives were to determine the frequency and accuracy of the alerts.Methods: Feasibility was assessed by the following steps: recruiting older adults with asthma, COPD and heart failure from primary care practices;developing software for extracting AQHI data from the Health Canada database;registering patients on the automatic dispatch messages system; andautomatically sending AQHI forecast alerts of moderate health risk or above to participants’ cell-phones the preceding night.Results: We successfully queried the Environment Canada database, detected AQHI alerts and delivered them to participants. Forecast alerts of moderate health risk were higher in summer and winter 2018-2019 in the study areas. The accuracy of AQHI forecast alerts for North Toronto versus Downtown Toronto were 81.7% (75.9 − 86.6%) and 80.7% (74.8 − 85.7%), respectively.Conclusions: Delivering AQHI alerts through text messages to patients in the primary care setting was feasible. Colder seasons should not be underestimated for moderate risk AQHI conditions.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".