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Predicting decreased health-related quality of life (HRQL) in adult survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).

2021· article· en· W3166977957 on OpenAlexaff
Fiona Schulte, Yan Chen, Yutaka Yasui, Wendy M. Leisenring, Todd M. Gibson, Paul C. Nathan, Kevin C. Oeffinger, Melissa M. Hudson, Gregory T. Armstrong, Leslie L. Robison, Kevin R. Krull, I‐Chan Huang

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick ChildrenUniversity of AlbertaUniversity of Calgary
FundersNational Institutes of HealthAmerican Lebanese Syrian Associated Charities
KeywordsMedicineLogistic regressionQuality of life (healthcare)DemographyConfidence intervalGerontologyEducational attainmentChildhood cancerCancerInternal medicine

Abstract

fetched live from OpenAlex

10043 Background: This study examines temporal patterns in HRQL among adult survivors of childhood cancer, and socio-demographic, lifestyle and health status predictors of decline in HRQL. Methods: Adult survivors of childhood cancer (4755, 55.2% female, 86.9% non-Hispanic white) completed baseline (T0) and follow-up (T1 in 2003, T2 in 2014) surveys (median[SD] age 32.4[7.5] at T1, time since diagnosis to T1 23.0[4.5], T1-T2 interval 11.7[0.6] years). Socio-demographic (e.g., age, sex, educational attainment, annual family income), lifestyle (physical inactivity, smoking) and health status predictors were collected at T0 and T1. Chronic conditions graded ≥2 by CTCAE defined as presence, and mental and cognitive status with ≥1SD from norms defined as poor. SF-36 Physical and Mental Component Summary (PCS/MCS; mean 50/SD 10) at T1 and T2 classified HRQL as optimal (≥40) or suboptimal ( < 40). Multivariable logistic regression identified risk factors (T0, T1 or status change T0-T1) of decreased HRQL (i.e., optimal to suboptimal) using a backward selection method (p < 0.1), adjusting for sex, race, age at T1 and years between T1-T2. The sample was randomly split into training (80%) and test (20%) datasets to develop and validate prediction models; Area Under the ROC Curve (AUC) evaluated model performance. Results: From T1-T2, 8.1% and 8.3% of survivors reported decreased PCS and MCS. AUCs of training/test models were 0.75/0.74 for decreased PCS and 0.72/0.68 for decreased MCS. Risk factors at T0 or T1 predicting decreased PCS included female sex (OR 1.67, 95%CI 1.25-2.24), younger age (OR 1.04, 95%CI 1.02-1.06), < college/vocational education (OR 1.59, 95%CI 1.02-2.46), family income < $20,000 (OR 2.00, 95%CI 1.21-3.30), obesity (OR 1.97, 95%CI 1.32-2.92), chronic health conditions (neurologic OR 2.47, 95%CI 1.69-3.60; musculoskeletal OR 2.27, 95%CI 1.42-3.64; endocrinological OR 2.25, 95%CI 1.44-3.52; gastrointestinal OR 1.89, 95%CI 1.32-2.69; pulmonary OR 1.66, 95%CI 1.06-2.59; cardiovascular OR 1.53, 95%CI 1.14-2.06) and depression (OR 1.79, 95%CI 1.20-2.67). Risk factors at T0 or T1 predicting decreased MCS included unemployment (OR 1.68, 95%CI 1.19-2.38), smoking (OR 2.03, 95%CI 1.37-3.00), physical inactivity (OR 1.48, 95%CI 1.05-2.09), poor mental health (depression OR 4.29, 95%CI 2.44-7.55; somatization OR 1.63, 95%CI 1.05-2.53) and poor cognitive status (task efficiency OR 1.90, 95%CI 1.34-2.68; organization OR 1.67, 95%CI 1.12-2.48). Conclusions: Nearly 10% of childhood cancer survivors have significant late-onset decline in HRQL. Chronic health conditions predict decreased physical HRQL, whereas smoking, physical inactivity and poor mental health predict decreased mental HRQL. Interventions targeting modifiable lifestyle and health conditions should be considered to prevent decreased HRQL for childhood cancer survivors.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.125
GPT teacher head0.475
Teacher spread0.350 · 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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