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Record W1989606086 · doi:10.5539/ijsp.v2n1p44

Analysis of Multiple Myeloma Life Expectancy Using Copula

2013· article· en· W1989606086 on OpenAlexvenueno aff
Seung-Hwan Lee, Eunjoo Lee

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2013
Typearticle
Languageen
FieldMathematics
TopicStatistical Distribution Estimation and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCopula (linguistics)Multiple myelomaLife expectancyMultivariate statisticsStatisticsBivariate analysisUnivariateEconometricsMedicineMathematicsInternal medicinePopulation

Abstract

fetched live from OpenAlex

Multiple myeloma is a blood cancer that develops in the bone marrow. It is assumed that in most cases multiple myeloma develops in association with several medical factors acting together, although the leading cause of the disease has not yet been identified. In this paper, we investigate the relationship between the factors to measure multiple myeloma patients' survival time. For this, we employ a copula that provides a convenient way to construct statistical models for multivariate dependence. Through an approach via copulas, we find the most influential medical factors that affect the survival time. Some goodness-of-fit tests are also performed to check the adequacy of the copula chosen for the best combination of the survival time and the medical factors. Using the Monte Carlo simulation technique with the copula, we re-sample survival times from which the anticipated life span of a patient with the disease is calculated.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.071
GPT teacher head0.369
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2013
Admission routes1
Has abstractyes

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