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Record W4361303402 · doi:10.1177/14799731221139293

Experiences and perceptions of receiving and prescribing rehabilitation in adults with cystic fibrosis undergoing lung transplantation

2023· article· en· W4361303402 on OpenAlexaff
Lisa Wickerson, Rajan Grewal, L.G. Singer, Cecilia Chaparro

Bibliographic record

VenueChronic Respiratory Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineThematic analysisRehabilitationLung transplantationCystic fibrosisHealth careTransplantationPhysical therapyMental healthPerceptionQualitative researchPsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Rehabilitation is prescribed to optimize fitness before lung transplantation (LTx) and facilitate post-transplant recovery. Individuals with cystic fibrosis (CF) may experience unique health issues that impact participation. METHODS: Patient and healthcare provider semi-structured interviews were administered to explore perceptions and experiences of rehabilitation before and after LTx in adults with CF. Interviews were analyzed via inductive thematic analysis. RESULTS: Eleven participants were interviewed between February and October 2021 (five patients, median 28 (IQR 27-29) years, one awaiting re-LTx, four following first or second LTx) and six healthcare providers. Rehabilitation was delivered both in-person and virtually using a remote monitoring App. Six key themes emerged: (i) structured exercise benefits both physical and mental health, (ii) CF-specific physiological impairments were a large barrier, (iii) supportive in-person or virtual relationships facilitated participation, (iv) CF-specific evidence and resources are needed, (v) tele-rehabilitation experiences during the COVID-19 pandemic resulted in preferences for a hybrid model and (vi) virtual platforms and clinical workflows require further optimization. There was good engagement with remote data entry alongside satisfaction with virtual support. CONCLUSIONS: Structured rehabilitation provided multiple benefits and a hybrid model was preferred going forward. Future optimization of tele-rehabilitation processes and increased evidence to support exercise along the continuum of CF care are needed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2023
Admission routes1
Has abstractyes

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