Bibliographic record
Abstract
Patient adherence to treatment in chronic obstructive pulmonary disease (COPD) is essential to optimise disease management. As with other chronic diseases, poor adherence is common and results in increased rates of morbidity, healthcare expenditures, hospitalisations and possibly mortality, as well as unnecessary escalation of therapy and reduced quality of life. Examples include overuse, underuse, and alteration of schedule and doses of medication, continued smoking and lack of exercise. Adherence is affected by patients' perception of their disease, type of treatment or medication, the quality of patient provider communication and the social environment. Patients are more likely to adhere to treatment when they believe it will improve disease management or control, or anticipate serious consequences related to non-adherence. Providers play a critical role in helping patients understand the nature of the disease, potential benefits of treatment, addressing concerns regarding potential adverse effects and events, and encouraging patients to develop self-management skills. For clinicians, it is important to explore patients' beliefs and concerns about the safety and benefits of the treatment, as many patients harbour unspoken fears. Complex regimens and polytherapy also contribute to suboptimal adherence. This review addresses adherence related issues in COPD, assesses current efforts to improve adherence and highlights opportunities to improve adherence for both providers and patients.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".