Effects of Self-Management Interventions in People With Interstitial Lung Disease
Bibliographic record
Abstract
BACKGROUND: People with interstitial lung disease (ILD) want to actively manage their condition; however, the effects of self-management interventions (SMIs) in this population have not been synthesized. This review summarizes the effects of SMIs on health-related quality of life (HRQOL), functional status, psychological and social factors, symptoms, exacerbations, health care utilization, and survival in people with ILD. METHODS: The protocol of this systematic review was registered (PROSPERO ID: CRD42022329199). Six digital databases were searched in May 2022 with monthly updates until August 2023. Studies implementing SMIs in people with any type of ILD were included. Risk of bias (RoB) and quality of evidence were assessed with the Cochrane tool for RoB assessment and the grading of recommendations, assessment, development, and evaluations. Meta-analysis was used to summarize the results. RESULTS: .58) nor in the secondary outcomes. No evidence for the effects of SMIs on functional capacity, exacerbations, and survival was found. The quality of evidence ranged from low to very low. CONCLUSIONS: There is low to very low-quality evidence that SMIs have no effect in people with ILD. However, this conclusion is limited by high methodological heterogeneity. A consensus definition of SMIs is needed to implement more comparable interventions and strengthen results.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".