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Record W4404433639 · doi:10.1177/12034754241293131

National Trends in Healthcare Expenditures for the Management of Skin Cancer in the United States

2024· article· en· W4404433639 on OpenAlexaff
Bryan Ma, Matthew T. James, An‐Wen Chan, P. Régine Mydlarski

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsWomen's College HospitalUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsMedicineSkin cancerCancerHealth careMelanomaPopulationDisease burdenAmbulatoryEnvironmental healthGerontologyDemographyDermatologyInternal medicineEconomic growthCancer research

Abstract

fetched live from OpenAlex

BACKGROUND: Malignant melanoma and keratinocyte carcinomas account for a substantial proportion of healthcare expenditures in the United States. OBJECTIVE: To estimate trends in the economic burden of skin cancer in the United States between 1996 and 2016. METHODS: The Disease Expenditure Project and Global Burden of Disease databases were used to estimate annual total costs and population-standardized rates of change for skin cancer-related healthcare spending. RESULTS: Skin cancer expenditures totaled $23.4 billion in 2016, of which $1.4 billion (95% CI: $1.3-$1.6 billion) was melanoma-related and $22 billion (95% CI: $18-$28 billion) was keratinocyte carcinoma-related. Most spending on skin cancer management occurred in ambulatory care settings [60.7% (95% CI: 57.7%-64.3%) for melanoma and 87.8% (95% CI: 87.2%-88.2%) for keratinocyte carcinoma]. Pharmaceutical costs for melanoma have increased since 2010 to $365 million (95% CI: $327-$416 million), which represents 26.1% (95% CI: 22.6%-29.3%) of total melanoma expenditure. CONCLUSIONS: Skin cancer management in the United States is costly. Expenditures have increased substantially since 1996 without signs of plateauing in recent years.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.048
GPT teacher head0.367
Teacher spread0.319 · 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 designNot applicable
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

Citations5
Published2024
Admission routes1
Has abstractyes

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