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Record W73070454 · doi:10.1177/082585970502100311

Assessing Quality of Life of Patients with Advanced Chronic Obstructive Pulmonary Disease in the End of Life

2005· article· en· W73070454 on OpenAlexaboutno aff
Samantha Pang, Kin-Sang Chan, Betty Pui Man Chung, Kam-Shing Lau, Edward Leung, Amanda W. K. Leung, Helen Y. L. Chan

Bibliographic record

VenueJournal of Palliative Care · 2005
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsQuality of life (healthcare)Psychological interventionMedicinePalliative careCOPDDistressPulmonary diseasePhysical therapyPsychologyClinical psychologyNursingInternal medicine

Abstract

fetched live from OpenAlex

Given the limitations of existing health-related quality-of-life (QOL) measures in capturing the end-of-life experience of patients with advanced chronic diseases, an empirically grounded instrument, the quality-of-life concerns in the end of life questionnaire (QOLC-E), was developed. Though it was built on the McGill quality of life questionnaire (MQOL), its sphere is more holistic and culturally specific for the Chinese patients in Hong Kong. One hundred and forty-nine patients with advanced chronic obstructive pulmonary disease (COPD) or metastatic cancer completed the questionnaire. Seven factors (28 items) which emerged from the factor analysis were grouped into four positive (support, value of life, food-related concerns, and healthcare concerns) and four negative (physical discomfort, negative emotions, sense of alienation, and existential distress) subscales. Good internal consistency and concurrent validity were shown. The results also revealed that these two groups of patients had similar QOL concerns. The validity of applying QOLC-E as an outcome measure to evaluate the effectiveness of palliative and psychoexistential interventions has yet to be tested.

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)0.002
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 score0.013
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.088
GPT teacher head0.419
Teacher spread0.331 · 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

Citations41
Published2005
Admission routes1
Has abstractyes

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