eHealth in Self-Managing at a Distance Patients with COPD
Bibliographic record
Abstract
Worldwide, healthcare delivery for chronic diseases has been challenging due to the current SARS-COV-2 pandemic. The growing use of information and communication technologies via telehealth has gained popularity in all fields of medicine. In chronic respiratory diseases, self-management, defined as a structured but personalized multi-component intervention with the main goal of achieving healthy behavioral change, is an essential element of long-term care. Iterative interventions delivered by a well-trained health coach in order to empower and provide the patient with the tools and skills needed to adopt sustained healthy behaviors have proven to be effective in chronic obstructive pulmonary disease (COPD). Benefits have been shown to both improve patient quality of life and reduce acute exacerbation events and acute healthcare utilization. In COPD, the evidence so far has shown us that remote technologies such as telemonitoring or remote management may improve patient-reported outcomes and healthcare utilization. However, clear limitations are still present and questions remain unanswered. More and better designed studies are therefore necessary to define the place of eHealth in self-managing at a distance in patients with COPD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".