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Record W2294244712 · doi:10.3747/co.23.2914

Cost and Resource Utilization in Cervical Cancer Management: A Real-World Retrospective Cost Analysis

2016· article· en· W2294244712 on OpenAlexafffundvenue
Ian Cromwell, Zenia Ferreira, Laurie Smith, K. van der Hoek, Gina Ogilvie, Andrew J. Coldman, Stuart Peacock

Bibliographic record

VenueCurrent Oncology · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsUniversity of British ColumbiaSimon Fraser UniversityCanadian Centre for Applied Research in Cancer ControlBC Cancer Agency
FundersHealth CanadaCanadian Centre for Applied Research in Cancer Control
KeywordsMedicineCervical cancerReal world dataCost analysisData scienceOperations managementCancerOperations researchComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: We set out to assess the health care resource utilization and cost of cervical cancer from the perspective of a single-payer health care system. METHODS: Retrospective observational data for women diagnosed with cervical cancer in British Columbia between 2004 and 2009 were analyzed to calculate patient-level resource utilization patterns from diagnosis to death or 5-year discharge. Domains of resource use within the scope of this cost analysis were chemotherapy, radiotherapy, and brachytherapy administered by the BC Cancer Agency; resource utilization related to hospitalization and outpatient visits as recorded by the B.C. Ministry of Health; medically required services billed under the B.C. Medical Services Plan; and prescriptions dispensed under British Columbia's health insurance programs. Unit costs were applied to radiotherapy and brachytherapy, producing per-patient costs. RESULTS: The mean cost per case of treating cervical cancer in British Columbia was $19,153 (standard error: $3,484). Inpatient hospitalizations, at 35%, represented the largest proportion of the total cost (95% confidence interval: 32.9% to 36.9%). Costs were compared for subgroups of the total cohort. CONCLUSIONS: As health care systems change the way they manage, screen for, and prevent cervical cancer, cost-effectiveness evaluations of the overall approach will require up-to-date data for resource utilization and costs. We provide information suitable for such a purpose and also identify factors that influence costs.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.136
GPT teacher head0.371
Teacher spread0.235 · 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 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

Citations11
Published2016
Admission routes3
Has abstractyes

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