Wildfire Smoke and Its Impact on Your Physical Therapy Practice
Bibliographic record
Abstract
SYNOPSIS: Wildfires are increasing in frequency and intensity, posing significant challenges to public health and health systems. Wildfires might directly influence how musculoskeletal rehabilitation clinicians prescribe exercise therapy, manage outdoor sessions, and maintain a safe clinic environment. These changes to musculoskeletal rehabilitation practice are particularly relevant given that clinicians often care for populations at greater risk of adverse health outcomes from exposure to wildfire smoke, including older adults and outdoor athletes. In this Viewpoint, we describe the role physical therapists can play in mitigating the health impacts of wildfires—through prevention and rehabilitation strategies—and emphasize the need for more proactive, evidence-based approaches. Recommendations include understanding who is at risk, implementing assessment practices for the negative effects of smoke, integrating air quality monitoring into clinical practice, adapting treatment protocols during wildfire seasons, and developing emergency preparedness to respond effectively to wildfire-related health challenges. We advocate for clinic-level policy changes, disaster preparedness training, and equitable resource allocation. J Orthop Sports Phys Ther 2025;55(11):690-694. Epub 17 September 2025. doi:10.2519/jospt.2025.13546
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".