A prospective evaluation of patient perspectives and financial considerations during prostate cancer treatment decision-making
Bibliographic record
Abstract
INTRODUCTION: In universal healthcare systems, patients may still encounter financial obstacles from cancer treatments, potentially influencing treatment decision-making. We investigated the relationship between socioeconomic status and treatment decision-making as it pertains to patient values, preferences, and perceived barriers to care for localized prostate cancer. METHODS: We conducted a prospective study of patients undergoing a prostate biopsy for the initial detection of prostate cancer. Sociodemographic variables were collected, with validated instruments used to determine health literacy levels. Patients were divided into two groups using self-reported income; those with a positive identification of prostate cancer underwent additional surveys to ascertain their knowledge of their diagnosis, treatment-related preferences, and socioeconomic barriers to care. Descriptive statistics were used. RESULTS: Of 160 patients, approximately one-third were classified as having low health literacy. Within the low-income group, education levels were lower (34.6% had less than high school education vs. 10.2% in the high-income group) and unemployment rates higher (75.0% unemployed vs. 38.9% in the high-income group). Low-income patients with prostate cancer placed greater importance on indirect out-of-pocket expenses related to treatment (78.3% vs. 33.3%, p=0.001), higher emphasis on treatment-related travel time (50% vs. 15.1%, p=0.004), and more often had difficulty paying for healthcare services in the past (30.9% vs. 9.1%, p=0.02). CONCLUSIONS: Patients with lower household incomes have unique treatment values and decision-making preferences. They may experience additional challenges and barriers to obtaining cancer care, at least partly related to indirect costs. These findings should be considered when framing prostate cancer treatment discussions and designing patient-facing health information.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".