Effective deployment of technology-supported management of chronic respiratory conditions: a call for stakeholder engagement
Bibliographic record
Abstract
BACKGROUND: Healthcare systems are under increasing strain, predominantly due to chronic non-communicable diseases. Connected healthcare technologies are becoming ever more capable and their components cheaper. These innovations could facilitate both self-management and more efficient use of healthcare resources for common respiratory diseases such as asthma and chronic obstructive pulmonary disease. However, newer technologies can only facilitate major changes in practice, and cannot accomplish them in isolation. FOCUS OF REVIEW: There are now large numbers of devices and software offerings available. However, the potential of such technologies is not being realised due to limited engagement with the public, clinicians and providers, and a relative paucity of evidence describing elements of best practice in this complex and evolving environment. Indeed, there are clear examples of wasted resources and potential harm. We therefore call on interested parties to work collaboratively to begin to realize the potential benefits and reduce the risks of connected technologies through change in practice. We highlight key areas where such partnership can facilitate the effective and safe use of technology in chronic respiratory care: developing data standards and fostering inter-operability, making collaborative testing facilities available at scale for small to medium enterprises, developing and promoting new adaptive trial designs, developing robust health economic models, agreeing expedited approval pathways, and detailed planning of dissemination to use. CONCLUSION: The increasing capability and availability of connected technologies in respiratory care offers great opportunities and significant risks. A co-ordinated collaborative approach is needed to realize these benefits at scale. Using newer technologies to revolutionize practice relies on widespread engagement and cannot be delivered by a minority of interested specialists. Failure to engage risks a costly and inefficient chapter in respiratory care.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".