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Record W3042164741 · doi:10.1002/jha2.62

Quality of life and socioeconomic indicators associated with survival of myeloid leukemias in Canada

2020· article· en· W3042164741 on OpenAlexafffundabout
Sonya Cressman, Donna E. Hogge, Mark D. Minden, Stephen Couban, Aly Karsan, Raewyn Broady, Emily McPherson, Khalif Halani, Jing Yi Weng, Stuart Peacock

Bibliographic record

VenueeJHaem · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsInnovative Targeting Solutions (Canada)Canada's Michael Smith Genome Sciences CentreUniversity of British ColumbiaQueen Elizabeth II Health Sciences CentreBC Cancer AgencySimon Fraser UniversityPrincess Margaret Cancer CentreTerry Fox Research InstituteVancouver General HospitalCanadian Centre for Applied Research in Cancer Control
FundersTerry Fox Research InstituteCanadian Centre for Applied Research in Cancer Control
KeywordsMedicineSocioeconomic statusQuality of life (healthcare)Proportional hazards modelMyeloid leukemiaInternal medicineMyelodysplastic syndromesDemographyGerontologyOncologyEnvironmental healthPopulationBone marrow

Abstract

fetched live from OpenAlex

Abstract Understanding how patient‐reported quality of life (QoL) and socioeconomic status (SES) relate to survival of acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) may improve prognostic information sharing. This study explores associations among QoL, SES, and survival through administration of the Euro‐QoL 5‐Dimension, 3‐level and Functional Assessment of Cancer Therapy‐Leukemia and financial impact questionnaires to 138 adult participants with newly diagnosed AML or MDS in a longitudinal, pan‐Canadian study. Cox regression and lasso variable selection models were used to explore associations among QoL, SES, and established predictors of survival. Secondary outcomes were changes in QoL, performance of the QoL instruments, and lost income. We found that higher QoL and SES were positively associated with survival. The Lasso model selected the visual analog scale of the EQ‐5D‐3L as the most important predictor among all other variables ( P = .03; 92% selection). Patients with AML report improved QoL after treatment, despite higher mean out‐of‐pocket expenditures compared with MDS (up to $599 CDN/month for AML vs $239 for MDS; P = .05), greater loss of productivity‐related income (reaching $1786/month for AML vs $709 for MDS; P < .05), and greater caregiver effects (65% vs 35% caregiver productivity losses for AML vs MDS; P < .05). Our results suggest that including patient‐reported QoL and socioeconomic indicators can improve the accuracy of survival models.

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)0.001
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 score0.045
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.033
GPT teacher head0.279
Teacher spread0.246 · 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

Citations3
Published2020
Admission routes3
Has abstractyes

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