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Record W4205893408 · doi:10.3390/life12010105

Weight Loss in Advanced Cancer: Sex Differences in Health-Related Quality of Life and Body Image

2022· article· en· W4205893408 on OpenAlexfundno aff
Charlotte Goodrose-Flores, Helén Eke, Stephanie E. Bonn, Linda Björkhem‐Bergman, Ylva Trolle Lagerros

Bibliographic record

VenueLife · 2022
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
FundersCanadian Arthritis Network
KeywordsCancerQuality of life (healthcare)Weight lossBody weightBiologyMedicineGerontologyPhysiologyDemographyInternal medicineObesitySociology

Abstract

fetched live from OpenAlex

Weight maintenance is a priority in cancer care, but weight loss is common and a serious concern. This study explores if there are sex differences in the perception of weight loss and its association to health-related quality of life (HRQoL) and body image. Cancer patients admitted to Advanced Medical Home Care were recruited to answer a questionnaire, including characteristics, the HRQoL-questionnaire RAND-36, and a short form of the Body Image Scale. Linear regression analyses stratified by sex and adjusted for age were performed to examine associations between percent weight loss and separate domains of HRQoL and body image score in men and women separately. In total, 99 participants were enrolled, of which 80 had lost weight since diagnosis. In men, an inverse association between weight loss and the HRQoL-domain physical functioning, β = -1.34 (95%CI: -2.44, -0.24), and a positive association with body image distress, β = 0.22 (95%CI: 0.07, 0.37), were found. In women, weight loss was associated with improvement in the HRQoL-domain role limitations due to physical health, β = 2.02 (95%CI: 0.63, 3.41). Following a cancer diagnosis, men appear to experience weight loss more negatively than women do. Recognizing different perceptions of weight loss may be of importance in clinical practice.

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)0.000
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 score0.023
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.050
GPT teacher head0.373
Teacher spread0.323 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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