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Record W4386208688 · doi:10.21037/jtd-23-209

The interplay of physical and cognitive function in rehabilitation of interstitial lung disease patients: a narrative review

2023· review· en· W4386208688 on OpenAlexaff
Masatoshi Hanada, Takako Tanaka, Ryo Kozu, Yuji Ishimatsu, Noriho Sakamoto, Ani Orchanian‐Cheff, Dmitry Rozenberg, W. Darlene Reid

Bibliographic record

VenueJournal of Thoracic Disease · 2023
Typereview
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsToronto General HospitalToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineRehabilitationCognitionNarrativeLung functionDiseaseInterstitial lung diseaseNarrative reviewPhysical medicine and rehabilitationPhysical therapyLungIntensive care medicinePathologyInternal medicinePsychiatryLiterature

Abstract

fetched live from OpenAlex

Background and Objective: Interstitial lung disease (ILD) encompasses several diverse pulmonary pathologies that result in abnormal diffuse parenchymal changes. When prescribing rehabilitation, several additional factors need to be considered as a result of aging, polypharmacy, and comorbidities manifested in ILD patients. This review aims to discuss issues related to frailty, skeletal muscle and cognitive function that limit physical activities in ILD patients. It will also highlight exercise training and propose complementary strategies for pulmonary rehabilitation. Methods: A literature search was performed in MEDLINE, CINAHL (inception to October 19th, 2022) using search terms based on concepts of: idiopathic pulmonary fibrosis or interstitial lung disease; frailty; muscular atrophy; skeletal muscle dysfunction; cognitive dysfunction; sleep quality; sleep disorders; anxiety disorders; or depressive disorders. After eligible texts were screened, additional references were included from references cited in the screened articles. Key Content and Findings: Frailty and skeletal muscle dysfunction are common in ILD. Weight loss, exhaustion, and anti-fibrotic medications can impact frailty, whereas physical inactivity, aging, corticosteroids and hypoxemia can contribute to sarcopenia (loss of muscle mass and function). Frailty is associated with worse clinical status, exercise intolerance, skeletal muscle dysfunction, and decreased quality of life in ILD. Sarcopenia appears to influence wellbeing and can potentially affect overall physical conditioning, cognitive function and the progression of ILD. Optimal assessment tools and effective strategies to prevent and counter frailty and sarcopenia need to be determined in ILD patients. Even though cognitive impairment is evident in ILD, its prevalence and underlying neurobiological model of contributing factors (i.e., inflammation, disease severity, cardiopulmonary status) requires further investigation. How ILD affects cognitive interference, motor control and consequently physical daily activities is not well defined. Strategies such as pulmonary rehabilitation, which primarily focuses on strength and aerobic conditioning have demonstrated improvements in ILD patient outcomes. Future incorporation of interval training and the integration of motor learning could improve transfer of rehabilitation strategies to daily activities. Conclusions: Numerous underlying etiologies of ILD contribute to frailty, skeletal muscle and cognitive function, but their respective neurobiologic mechanisms require further investigation. Exercise training increases physical measures, but complementary approaches may improve their applicability to improve daily activities.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.383
Teacher spread0.367 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations18
Published2023
Admission routes1
Has abstractyes

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