MétaCan
Menu
Back to cohort

An Exponential Melanoma Trend Model

2015· article· en· W2033253137 on OpenAlexvenueno aff
Örjan Hallberg

Bibliographic record

VenueInternational Journal of Statistics in Medical Research · 2015
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMelanomaPopulationExponential functionDemographyExponential growthLung cancerStatisticsMathematicsMedicineOncologyCancer research

Abstract

fetched live from OpenAlex

The present study investigated whether whole population exposure to radiation introduced by radio broadcasting and cell phone systems might explain recent increases in melanoma trends in Nordic countries or not. Trends were modeled using a single exponential function of the time each age group has been living in the new environment since an environmental change took place. The results clearly show that melanoma incidences started to increase exponentially by the time lived as an adult since 1955 and that a second trend break occurred in 1997. We searched best fit between calculated and reported age-standardized rates by parameter variation, and compared calculated with reported age-specific rates without further parameter adjustments. Local variations of breast cancer, lung cancer and all cancers together significantly correlated with corresponding local melanoma rates in Sweden. Increasing cancer trends since around 1997 seem related to a population covering environmental change effective from early 90’s. We conclude that this exponential trend model can be a useful tool in understanding responses to sudden environmental changes.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.181
GPT teacher head0.534
Teacher spread0.352 · 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 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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Statistics in Medical ResearchSame topicCancer Risks and FactorsFrench-language works237,207