An adaptive treatment recommendation and outcome prediction model for\n metastatic melanoma
Bibliographic record
Abstract
Melanoma is a type of skin cancer developed from melanocytes. It is one of\nthe most lethal types of cancer, accounting for approximately 75% of skin\ncancer deaths. Late stage melanoma is very difficult to treat, since the cancer\ncells are deranged, may be genetically linked and can be unresponsive to\ntherapy. Therefore, determining how to effectively make use of different\ntreatment regimens is of vital importance to survival. In this analysis, we\npropose an adaptive treatment recommendation system based on a hybrid\ncluster-classification (CC) structure. Our proposed system consists of two\nparts,1) distribution based clustering and 2) classification. Our\nrecommendation system can help to identify high-risk melanoma patients and\nsuggest the best approach to treatment, which enables clinicians and patients\nto make decisions on the basis of real-world data. Our data came from the\nCanadian Melanoma Research Network (CMRN) database, a pan-Canadian multi-year\nobservational database, which is part of Global Melanoma Registry Network\n(GMRN). Training/testing sets are generated based on data from different\nsources, leading to cross cohort analysis tasks. Experimental results show that\nour proposed system achieves very promising results with an overall accuracy of\nup to 80%.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".