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Record W2015547130 · doi:10.1097/ncc.0b013e31827b5bdc

Measurement of Quality of Life in Patients With End-Stage Cancer

2013· article· en· W2015547130 on OpenAlexfundno aff
Kyeong Uoon Kim

Bibliographic record

VenueCancer Nursing · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
FundersMcMaster University
KeywordsMedicineQuality of life (healthcare)Stage (stratigraphy)CancerInternal medicinePhysical therapyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer is the leading cause of death in Korean adults. A good quality of life for patients at end life can control pain and symptoms and help maintain well-being. OBJECTIVE: The aim of this study was to measure quality of life in end-stage cancer patients using the Korean version of the McMaster Quality of Life (K-MQOL). METHODS: The K-MQOL was administered to adult end-stage cancer patients from 4 Korean university hospitals. We hypothesized quality-of-life differences between participants by daily activity level, number of symptoms, and participant status (alive or not) at end of study. RESULTS: Participants' mean age was 49.2 years, and 74.5% were men. As hypothesized for discriminant validity, Pearson correlation coefficients among the K-MQOL were less than 0.4, with the exceptions of emotion (0.25-0.52) and cognition (0.33-0.51). A higher Eastern Cooperative Oncology Group Performance States Rating score was significantly associated with a lower quality of life (F = 2.840, P = 0.034). The mean score of those within 21 days of death was significantly lower than that of patients who were alive at the end of the study (t = -2.48, P = .04). Patients with a smaller number of symptoms other than pain had significantly higher quality-of-life scores than did those with more symptoms (F = 5.059, P = .004). CONCLUSIONS: The K-MQOL provided reliable and valid scores of quality of life in end-stage cancer patients. IMPLICATIONS FOR PRACTICE: Assessing end-stage cancer patients' quality of life helps to identify each patient's condition and aspects that could benefit from nursing care. We anticipate that the K-MQOL will be useful for patient assessment in clinical and community settings.

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 score0.039
Threshold uncertainty score0.975

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)0.0010.000

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 head0.050
GPT teacher head0.326
Teacher spread0.276 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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