Linking Intermediate to Final “Real-World” Outcomes: Is Financial Toxicity a Reliable Predictor of Poorer Outcomes in Cancer?
Bibliographic record
Abstract
Traditionally, economic evaluations are based on clinical trials with well-defined patient populations that exclude many patient types. By contrast, studies that incorporate general patient populations end up including those in lower income categories, some of whom have significant financial burdens (often described as financial toxicity) related to their care. Consideration of these patient burdens when examining the incremental cost-effectiveness of newer treatments from a clinical trial perspective can result in differing conclusions regarding cost-effectiveness. The challenge is to reliably assess the link between financial toxicity, quality of life and potential decisions to forego or delay care. It is also well-documented that these financial effects are not evenly distributed across populations, with those with low income and of black or Latino decent being most affected. There is a paucity of literature in this space, but some of the early work has suggested that for lung, breast, colorectal and ovarian cancers there are poorer quality-of-life scores and/or shorter overall survival for those experiencing financial toxicity. Hence, we may see both a lower quality of life and a shorter duration of life for these populations. If this is the case, additional considerations include: are the benefits of newer, more-expensive treatment strategies muted by the lack of adherence to these newer treatments due to financial concerns, and, if true, can these effects be effectively quantified as "real-world" outcomes? This rapid review examines these possibilities and the steps that may be required to examine this reliably.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".