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Record W169211968 · doi:10.1177/082585971202800404

Quality of Life for Hong Kong Chinese Patients with Advanced Gynecological Cancers in the Palliative phase of Care: A Cross-Sectional Study

2012· article· en· W169211968 on OpenAlexaboutno aff
Kwok Ying Chan, Man Lui Chan, Thomas Yau, Cho Wing Li, Hon Wai Benjamin Cheng, Mau Kwong Sham

Bibliographic record

VenueJournal of Palliative Care · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHospital Anxiety and Depression ScaleQuality of life (healthcare)PsychosocialDepression (economics)Palliative careCross-sectional studyAnxietyPhysical therapyInternal medicinePsychiatryNursing

Abstract

fetched live from OpenAlex

This study investigated the quality of life (QOL) of Hong Kong Chinese patients with advanced gynecological cancers (AGC). A cross-sectional study was conducted with 53 consecutive patients with AGC who were admitted to a university-based palliative care unit. The assessment tools utilized were: the McGill quality of life questionnaire for Hong Kong Chinese (MQOL-HK); the hospital anxiety and depression scale (HADS); the Palliative Performance Scale (PPS); and the psychosocial adjustment to illness scale (PAIS), sexual relationships subscale. The mean total score of the MQOL-HK was 4.63 +/- 1.94, within which the physical domain scored the worst (mean=3.99, SD=2.15, range: 0-7). Depression symptoms were common (62 percent). The median PPS was 40 percent. Younger age, higher HADS depression scores, and higher HADS anxiety scores were significantly correlated with poorer QOL. Furthermore, younger age and depression were significant predictors for a worse MQOL-HK score. In conclusion, Chinese patients with AGC have a relatively poor QOL, especially in the physical domain and in terms of depression symptoms. Age and depression symptoms are the most important factors affecting QOL. Proper identification of physical symptoms and depression symptoms, along with appropriate treatments, are important for improving QOL for patients.

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 score0.025
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.062
GPT teacher head0.429
Teacher spread0.368 · 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
Published2012
Admission routes1
Has abstractyes

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