European Respiratory Society statement on advanced telemedicine for obstructive sleep apnoea (e-Sleep)
Bibliographic record
Abstract
Telemedicine as a means of remote patient–physician interaction is gaining popularity in nearly every field, and (respiratory) sleep medicine is no exception. Because obstructive sleep apnoea (OSA) is a chronic condition, and requires a continuous treatment and monitoring of therapy success, telematic communications could be useful to establish diagnostic and therapeutic strategies. This statement summarises the evidence and efficacy of telemedicine options in OSA. An interdisciplinary European Respiratory Society (ERS) task force evaluated the scientific literature based on a systematic search and two-step screening process (title/abstract and full text). Although the task force does not make recommendations for clinical practice, it describes its current practice of telemedicine applications in OSA. The literature shows that telemedicine has been studied in different areas of OSA management, with potential benefits. Telemedicine also served as a major research tool to provide big data related to positive airway pressure therapy. Telemedicine results in similar or improved compliance when compared with traditional face-to-face encounters. Telemedicine-based targeted troubleshooting and support based on individual patient data, and a combination via smartphone apps or coaching websites, are feasible and effective. Expanding evidence suggests that telemedicine is probably cost-effective. However, data do not consistently support staff time savings through telemedicine-based solutions. The potential benefits of telemedicine include improved access to healthcare, and increased adherence to (chronic illness) treatment plans. Benefits should be weighed against the overall costs of telemedicine and risks related to suboptimal compliance.
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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.032 | 0.025 |
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".