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Record W4390340213 · doi:10.4187/respcare.11298

Effects of Self-Management Interventions in People With Interstitial Lung Disease

2023· review· en· W4390340213 on OpenAlexaff
Steve Freitas Dos Santos, Alda Marques, Patrícia Rebelo, Dina Brooks, Adam Benoit, Ana Oliveira

Bibliographic record

VenueRespiratory Care · 2023
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsWest Park Healthcare CentreUniversity of Toronto
Fundersnot available
KeywordsMedicinePsychological interventionInterstitial lung diseaseIntensive care medicineDiseaseLungLung diseasePhysical therapyInternal medicineNursing

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.328
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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