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Record W3011488375 · doi:10.12927/hcpol.2020.26129

Comparing Childhood Cancer Care Costs in Two Canadian Provinces

2020· article· en· W3011488375 on OpenAlexafffundvenueabout
Mary L. McBride, Claire de Oliveira, Ross Duncan, Karen E. Bremner, Mark Greenberg, Paul C. Nathan, Stuart Peacock, Murray Krahn

Bibliographic record

VenueHealthcare policy · 2020
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPediatric Oncology GroupUniversity Health NetworkHospital for Sick ChildrenUniversity of British ColumbiaCentre for Addiction and Mental HealthCanadian Centre for Applied Research in Cancer Control
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesCancer Care Ontario
KeywordsChildhood cancerHealth careCancerMedical costsGeographyMedicineEnvironmental healthBusinessEconomic growthEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Cancer in children presents unique issues for diagnosis, treatment and survivorship care. Phase-specific comparative cost estimates are important for informing healthcare planning. OBJECTIVE: The aim of this paper is to compare direct medical costs of childhood cancer by phase of care in British Columbia (BC) and Ontario (ON). METHODS: For cancer patients diagnosed at <15 years of age and propensity-score-matched non-cancer controls, we applied standard costing methodology using population-based healthcare administrative data to estimate and compare phase-based costs by province. RESULTS: Phase-specific cancer-attributable costs were 2%-39% higher for ON than for BC. Leukemia pre-diagnosis costs and annual lymphoma continuing care costs were >50% higher in ON. Phase-specific in-patient hospital costs (the major cost category) represented 63%-82% of ON costs, versus 43%-73% of BC costs. Phase-specific diagnostic tests and procedures accounted for 1.0%-3.4% of ON costs and 2.8%-13.0% of BC costs. CONCLUSION: There are substantial cost differences between these two Canadian provinces, BC and ON, possibly identifying opportunities for healthcare planning improvement.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.061
GPT teacher head0.401
Teacher spread0.340 · 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.

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

Citations8
Published2020
Admission routes4
Has abstractyes

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