Financial toxicity in hematological malignancies: a systematic review
Bibliographic record
Abstract
Hematologic malignancy outcomes have remarkably improved in the past decade with further advancement expected in future years. However, the detrimental effects of financial toxicity (FT) on patients with hematologic malignancies, because of both diagnoses and subsequent treatments, have not been studied comprehensively. We performed a systematic review of all studies reporting FT as a primary or secondary outcome among adult or pediatric patients with hematological malignancies. A total of 55 studies met the inclusion criteria for analysis. Across studies, 20-50% of patients reported some form of FT, including loss of work productivity, food and transportation costs, and depletion of savings. Younger age, lower-income level, unemployment, and rural residence were the most commonly identified risk factors for FT. Two studies looked at survival outcomes, with one reporting improvement in survival with a decrease in financial toxicity. However, significant heterogeneity in FT definitions was found between countries and payor systems. Only half of the studies (51%, n = 28) used validated survey instruments such as the COST assessment. The present systematic review identified that FT is common in patients with hematological malignancies and may be associated with poorer outcomes. However, studies of FT generally use non-standardized methods with cross-sectional analyses rather than longitudinal, prospective assessments. Further work is needed to standardize FT reporting and investigate measures to alleviate FT among patients with hematologic malignancies.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".