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Financial toxicity among patients with lung cancer in a publicly funded health care system.

2018· article· en· W2892576229 on OpenAlexaffabout
Doreen A. Ezeife, Joshua C. Morganstein, Sally C. M. Lau, Lisa W. Le, David Cella, Penelope Ann Bradbury, Geoffrey Liu, Adrian G. Sacher, Frances A. Shepherd, Natasha B. Leighl

Bibliographic record

VenueJournal of Clinical Oncology · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsBC Cancer AgencyUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineLogistic regressionCohortLung cancerMedicaidHealth careQuality of life (healthcare)Internal medicineCancerFinanceEmergency medicineNursing

Abstract

fetched live from OpenAlex

192 Background: Financial distress has been established as a clinically relevant patient-reported outcome (PRO) associated with worse mortality and quality of life, but remains under-recognized by health care providers. Our goal was to define factors associated with financial toxicity (FT) in a public healthcare system. Methods: Patients with advanced lung cancer were recruited from outpatient clinics at the Princess Margaret Cancer Centre (Toronto, Canada). FT was measured with the validated Comprehensive Score for Financial Toxicity (COST) instrument, a 12-item survey scored from 0-44 with lower scores reflecting worse financial well-being. Data on patient and treatment characteristics, total out-of-pocket costs (OOP) and extended insurance coverage (EIC) were collected. Multivariable logistic regression models were fit for COST score and each variable, to determine factors associated with greater FT (COST < 21). Results: Of 251 patients approached, 200 (80%) participated. Median age of the cohort was 65 years; 56% were female, 64% immigrants and 77% employed or on pension. Median total OOP while on treatment ranged between $1000-5000 CAD. Median COST score was 21 (range 0-44). FT was associated with age, with patients < 65 years reporting greater FT than older patients (COST 18.0 vs. 24.0, p < 0.0001). In multivariable logistic regression analysis, younger age was associated with greater FT, when adjusting for income, employment status, OOP and EIC (OR 3.6, [95% CI, 1.5-9.1]; p < 0.0001). Total OOP > $1000 and EIC also were associated with greater FT (adjusted OR 5.0 [95% CI, 2.0-12.1] and 3.7 [95% CI, 1.5-9.1], respectively). Conclusions: Age is significantly associated with FT in the Canadian (Ontario) public healthcare system, with younger lung cancer patients reporting greater financial distress. This study highlights priority patient populations where FT should be routinely assessed and appropriate resources for support offered.

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)0.005
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.382
Teacher spread0.318 · 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
Published2018
Admission routes2
Has abstractyes

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