Financial toxicity among patients with lung cancer in a publicly funded health care system.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".