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Record W2785160602 · doi:10.14740/jocmr3248w

Validation of the Graded Prognostic Assessment for Melanoma Using Molecular Markers (Melanoma-molGPA)

2018· article· en· W2785160602 on OpenAlexvenueno aff
Carsten Nieder, Mandy Hintz, Angelika Bilger, Oliver Oehlke, Anca‐Ligia Grosu

Bibliographic record

VenueJournal of Clinical Medicine Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMelanomaConfidence intervalLog-rank testOncologyInternal medicineSurvival analysisOverall survivalRetrospective cohort studyNodular melanomaCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: It has been suggested to replace the diagnosis-specific graded prognostic assessment (DS-GPA, based on performance status and number of brain metastases) for patients with primary malignant melanoma with the new Melanoma-molGPA. The latter is a more complex assessment, which also includes BRAF mutation status, age and extracranial metastases. To test the performance of the Melanoma-molGPA, we performed a validation study of this new survival prediction tool. METHODS: A retrospective analysis of patients treated at two different academic institutions was performed. The four-tiered Melanoma-molGPA was calculated as suggested in the original study. RESULTS: Median overall survival was 5.4 months (95% confidence interval: 3.1 - 7.7 months). Median survival in the four prognostic classes was 2.1, 7.8, 11.8, and 18.0 months, respectively. The 1-year survival rates were 3%, 25%, 43%, and 80%, respectively. The difference between the Kaplan-Meier curves was significant (P = 0.0001, log-rank test). CONCLUSIONS: The present survival outcomes support the use of the Melanoma-molGPA. However, survival was better in each of the four groups in the original study. Possible reasons include lead-time bias and different treatment policies.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.184
GPT teacher head0.513
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2018
Admission routes1
Has abstractyes

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