Incremental costs of prostate cancer trials: Are clinical trials really a burden on a public payer system?
Bibliographic record
Abstract
INTRODUCTION: Clinical trials are a critical component of improving cancer prevention and treatment strategies. However, the perception that patients enrolled in trials consume more resources than those receiving the standard-of-care (SOC) has contributed to an increasingly research-averse environment. Current economic data pertaining to the per-patient costs of prostate cancer trials relative to SOC treatment are limited. METHODS: A retrospective observational cohort study was conducted to compare costs incurred by 59 prostate cancer patients participating in a mix of industry and non-industry sponsored clinical trials with costs incurred by an equal number of eligible non-participants who received SOC over a year. Resource utilization was tracked and quantified to standardized price templates. RESULTS: No difference in overall resource utilization was seen between trial and SOC patients (two-tailed t-test, n = 118, p = 0.99). Variability in the types of resources used by each group indicated that, while trial patients may take up significantly more clinic time (p = 0.001) and undergo more tests and procedures (p = 0.001), SOC patients are more likely to receive other costly interventions, such as radiation therapy (p < 0.001). Other variables (e.g., pathology, diagnostic imaging, prescribed therapies) were statistically indistinguishable between groups. CONCLUSION: This study revealed differences in the cost distribution of patients enrolled in clinical trials versus those receiving SOC, which could be used to improve resource allocation. The lack of evidence for a difference in overall cost provides an argument for payers to more fully support clinical research without fear of adverse financial consequences. Further analysis is required.
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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 | 0.094 | 0.459 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".