MétaCan
Menu
Back to cohort
Record W4214630906 · doi:10.1111/crj.13484

The impact of COVID‐19 upon the delivery of exercise services within cystic fibrosis clinics in the United Kingdom

2022· article· en· W4214630906 on OpenAlexaff
Owen W. Tomlinson, Zoe L. Saynor, Daniel Stevens, Don S. Urquhart, Craig A. Williams

Bibliographic record

VenueThe Clinical Respiratory Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsDalhousie University
FundersCystic Fibrosis Trust
KeywordsTelehealthMedicinePandemicCoronavirus disease 2019 (COVID-19)Cystic fibrosisTelemedicineAdaptation (eye)Healthcare delivery2019-20 coronavirus outbreakPhysical therapyFamily medicineHealth careNursingInternal medicineDiseasePathology

Abstract

fetched live from OpenAlex

OBJECTIVES: The COVID-19 pandemic has resulted in unprecedent changes to clinical practice, and as the impact upon delivery of exercise services for people with cystic fibrosis (CF) in the United Kingdom was unknown, this was characterised via a national survey. METHODS: An electronic survey was distributed to healthcare professionals involved in the exercise management of CF via established professional networks. RESULTS: In total, 31 CF centres participated. Findings included significant reductions in exercise testing and widespread adaptation to deliver exercise training using telehealth methods. Promisingly, 71% stated that they would continue using virtual methods of engaging patients in future practice. CONCLUSION: These findings highlight adaptation to the COVID-19 pandemic and the need to develop sustainable and standardised telehealth services to manage patients moving forwards.

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.090
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.112
GPT teacher head0.451
Teacher spread0.339 · 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 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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Clinical Respiratory JournalSame topicCystic Fibrosis Research AdvancesFrench-language works237,207