Estimating healthcare resource use associated with the treatment of metastatic melanoma in eight countries
Bibliographic record
Abstract
Objectives Studies reporting healthcare resourse use (HRU) for melanoma, one of the most costly cancers to treat, are limited. Using consistent, robust methodology, this study estimated HRU associated with the treatment of metastatic melanoma in eight countries. Methods Using published literature and clinician input, treatment phases were identified: active systemic treatment (pre-progression); disease progression; best supportive care (BSC)/palliative care; and terminal care. HRU elements were identified for each phase and estimates of the magnitude and frequency of use in clinical practice were obtained through country-specific Delphi panels, comprising healthcare professionals with experience in oncology (n = 8). Results Medical oncologists are the key care providers for patients with metastatic melanoma, although in Germany dermato-oncologists also lead care. During the active systemic treatment phase, each patient was estimated to require 0.83-2 consultations with a medical oncologist/month across countries; the median number of such assessments in 3 months was highest in Canada (range = 3.5-5) and lowest in France, the Netherlands and Spain (1). Resource use during the disease progression phase was intensive and similar across countries: all patients were estimated to consult with medical oncologists and 10-40% with a radiation oncologist; up to 40% were estimated to require a brain MRI scan. During the BSC/palliative care phase, all patients were estimated to consult with medical oncologists, and most to consult with a primary care physician (40-100%). Limitations Panelists were from centers of excellence, thus results may not reflect care within smaller hospitals; data obtained from experts may be less variable than data from broader clinical practice. Treatments for metastatic melanoma are continually emerging, thus some elements of our work could be superseded. Conclusions HRU estimates were substantial and varied across countries for some resources. These data could be used with country-specific costs to elucidate costs for the management of metastatic melanoma.
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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 | 0.000 |
| 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".