Economic burden of brain metastases among patients with metastatic melanoma in a USA managed care population
Bibliographic record
Abstract
Malignant melanoma patients frequently relapse with metastases in the brain, making it the third most common cancer-causing brain metastases in the USA. Management of brain metastases remains challenging because of the rapid progression of disease and ineffectiveness of conventional therapies. This retrospective study, with a 'pre/post' design, quantifies the economic burden of brain metastases among melanoma patients in the USA. A large managed-care insurance claims database (2000 Q1-2011 Q3) was used to identify patients with melanoma and brain metastases. The preperiod was defined as the 6 months before the index date (diagnosis of first observed brain metastases) and postperiod as the period following the index date up to 12 months. All-cause and brain metastasis-related healthcare resource utilization and healthcare costs were compared on a per-patient-per-month (PPPM) basis between preperiods and postperiods. The study included 6076 patients (mean age 63.4 years); 57.6% were men. Significant differences (P<0.0001) were observed between the postperiods and preperiods in the mean all-cause and brain metastasis-related PPPM hospitalizations and emergency department and outpatient visits. Significant postperiod versus preperiod differences were also observed in the PPPM mean (standard error) all-cause healthcare costs [total: $14 489 ($231) vs. $7277 ($116); inpatient: $6330 ($195) vs. $1900 ($69); outpatient: $6609 ($102) vs. $4449 ($79); P<0.0001 for all] and brain metastasis-related costs [total: $6542 ($145) vs. $1933 ($62); inpatient: $2976 ($118) vs. $472 ($39); outpatient: $3451 ($76) vs. $1413 ($47); P<0.0001 for all]. Radiotherapy was the most common treatment. The economic burden associated with brain metastases in melanoma is significant and underscores the need for newer therapies to improve outcomes in these patients.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".