The emerging role of digital health in the management of asthma
Bibliographic record
Abstract
The most common reasons seen for lack of asthma control include misconceptions about disease control, low controller treatment adherence, poor inhaler technique, and the resulting underuse of controllers and overuse of short-acting beta2 agonists (SABAs). Narrowing these care gaps may be achieved through well-designed patient education that considers the patient's motivation, beliefs, and capabilities regarding their asthma and its management and empowers the patient to become an active participant in treatment decisions. Digital health technologies (DHTs) and digital therapeutic (DT) devices provide new opportunities to monitor treatment behaviors, improve communication between healthcare providers and patients, and generate data that inform educational interactions. DHT and DT have been proven effective in enhancing patient self-management in other chronic conditions, particularly diabetes. Accelerated integration of DHT and DT into the management of asthma patients is facilitated by the use of digital inhalers that employ sensor technology ("smart" inhalers). These devices efficiently provide real-time feedback on controller adherence, SABA use, and inhaler technique that have the strong potential to optimize asthma control.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".