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Record W2566634315 · doi:10.1002/ijc.30586

Identification and prediction of health‐related quality of life trajectories after a prostate cancer diagnosis

2016· article· en· W2566634315 on OpenAlexafffund
Megan S. Farris, Karen Kopciuk, Kerry S. Courneya, Sarah McGregor, Qinggang Wang, Christine M. Friedenreich

Bibliographic record

VenueInternational Journal of Cancer · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of AlbertaUniversity of CalgaryAlberta Health Services
FundersNational Cancer InstituteCanadian Cancer Society Research InstituteAlberta Cancer FoundationCanadian Institutes of Health ResearchAlberta InnovatesFondation pour la Recherche MédicaleCanada Research Chairs
KeywordsProstate cancerIdentification (biology)MedicineProstateQuality of life (healthcare)CancerProstate diseaseOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

The aim of our study was to identify physical and mental health-related quality of life (HRQoL) trajectories after a prostate cancer diagnosis and systematically characterize trajectories by behaviours and prognostic factors. Prostate cancer survivors (n = 817) diagnosed between 1997 and 2000 were recruited between 2000 and 2002 into a prospective repeated measurements study. Behavioural/prognostic data were collected through in-person interviews and questionnaires. HRQoL was collected at three post-diagnosis time-points, approximately 2 years apart using the Short Form (SF)-36 validated questionnaire. To identify physical and mental HRQoL trajectories, group-based trajectory modelling was undertaken. Differences between groups were evaluated by assessing influential dropouts (mortality/poor health), behavioural/prognostic factors at diagnosis or during the follow-up. Three trajectories of physical HRQoL were identified including: average-maintaining HRQoL (32.2%), low-declining HRQoL (40.5%) and very low-maintaining HRQoL (27.3%). In addition, three trajectories for mental HRQoL were identified: average-increasing HRQoL (66.5%), above average-declining HRQoL (19.7%) and low-increasing HRQoL (13.8%). In both physical and mental HRQoL, dropout from mortality/poor health differed between trajectories, thus confirming HRQoL and mortality were related. Furthermore, increased Charlson comorbidity index score was consistently associated with physical and mental HRQoL group membership relative to average maintaining groups, while behaviours such as time-varying physical activity was associated with physical HRQoL trajectories but not mental HRQoL trajectories. It was possible to define three trajectories of physical and mental HRQoL after prostate cancer. These data provide insights regarding means for identifying subgroups of prostate cancer survivors with lower or declining HRQoL after diagnosis whom could be targeted for interventions aimed at improving HRQoL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.027
GPT teacher head0.348
Teacher spread0.321 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2016
Admission routes2
Has abstractyes

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