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Record W2158425213 · doi:10.1183/09031936.02.01852001

The effect of postrehabilitation programmes among individuals with chronic obstructive pulmonary disease

2002· article· en· W2158425213 on OpenAlexafffund
Dina Brooks, B. Krip, Suzanna Mangovski-Alzamora, Roger Goldstein

Bibliographic record

VenueEuropean Respiratory Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of TorontoWest Park Healthcare Centre
FundersMedical Research CouncilCanadian Lung AssociationPhysiotherapy Foundation of Canada
KeywordsMedicinePulmonary diseaseCOPDPhysical therapySignificant differenceQuality of life (healthcare)Pulmonary rehabilitationRehabilitationInternal medicineNursing

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effects of two post-rehabilitation programmes on functional exercise tolerance and health-related quality of life in patients with chronic obstructive pulmonary disease (COPD). Subjects with COPD (n=109) were randomised to receive either enhanced follow-up (EF) or conventional follow-up (CF). Subjects in the EF group attended a monthly support group and received a telephone call from a staff member at the midpoint (2 weeks) between their visits. Both groups had scheduled appointments with a physical therapist and physician at 3-monthly intervals after discharge. Longitudinal data were recorded in 85 subjects (37 EF and 48 CF). Over the course of the study, there was no difference in distance walked in 6 min between the two groups but a significant difference for time and a group-time interaction. There was no difference in total chronic respiratory disease questionnaire score between groups at baseline or at any time interval despite a significant difference with time. There was a clear deterioration in functional exercise capacity and health-related quality of life after completion of respiratory rehabilitation but no difference between the groups.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.157
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.266
Teacher spread0.253 · 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

Citations217
Published2002
Admission routes2
Has abstractyes

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