Economic burden of toxicities associated with treating metastatic melanoma in eight countries
Bibliographic record
Abstract
BACKGROUND: Information on costs of managing adverse events (AEs) associated with current treatments in metastatic melanoma is limited. This study estimates costs of AEs in eight countries: Australia (AU), Canada (CA), France (FR), Germany (GE), Italy (IT), the Netherlands (NL), Spain (ES), and the UK. METHODS: A literature search was conducted to identify grade 3/4 AEs from product label, published trials, conference abstracts, and treatment guidelines. Resource utilization for the management of each type of AE was determined via interviews with 5 melanoma clinicians in each country. Outpatient and inpatient costs were estimated for each type of AE using country-specific tariffs or government/published sources. RESULTS: In outpatient settings, the most costly AEs per incident included cutaneous squamous cell carcinoma (CSCC) (€1063, £720; NL/UK), anemia (€1443, €1329, €1285; ES/IT/FR), peripheral neuropathy (€1289; ES), and immune-related diarrhea (AUS$1,121; AU). In inpatient settings, the most costly AEs per hospitalization included hypophysitis (€10,265; €5316; CAN$9735; AUS$7231: ES/FR/CA/AU), dyspnea (€9077; GE), elevated liver enzymes (€6913, CAN$8030, AUS$6594; FR/CA/AU), CSCC (CAN$8934; CA), peripheral neuropathy (€6977, €4144, CAN$9472; NL/ES/CA), and diarrhea (£4284, €4113; UK/ES). CONCLUSIONS: Costs of managing AEs can be significant, and thus effective treatments with lower rates of severe AEs would be valuable.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".