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Record W2779497146 · doi:10.1097/jom.0000000000001242

The Economic Burden of Bladder Cancer Due to Occupational Exposure

2017· article· en· W2779497146 on OpenAlexaboutno aff
Young Jung, Emile Tompa, Christopher J. Longo, Christina Kalcevich, Joanne Kim, Chaojie Song, Paul A. Demers

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBladder cancerMedicineIndirect costsEnvironmental healthTotal costEconomic costOccupational cancerQuality of life (healthcare)CancerQuality-adjusted life yearOccupational exposureDemographyCost effectivenessInternal medicineRisk analysis (engineering)BusinessEconomics

Abstract

fetched live from OpenAlex

OBJECTIVE: To estimate the economic burden of bladder cancer due to occupational exposures. METHODS: Using a societal perspective, we estimate the lifetime costs of newly diagnosed cases of bladder cancer in Canada that is associated with occupational exposure for the calendar year 2011. The three major categories we consider are direct, indirect, and quality of life costs. RESULTS: There were 199 newly identified cases of bladder cancer. The estimated total cost of bladder cancer for new cases in 2011 was $131 million and an average per-case cost of $658,055 CAD (2011 dollars). Of the total costs, direct costs accounted for 6%, indirect costs 29%, and health-related quality of life costs 65%. CONCLUSIONS: The per-case economic burden of bladder cancer due to occupational exposure is substantial which suggests the importance and value of exposure reduction.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.323
Teacher spread0.294 · 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 designObservational
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

Citations18
Published2017
Admission routes1
Has abstractyes

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