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Record W2132574271 · doi:10.1017/s0266462305050610

Marginal cost of operating a positron emission tomography center in a regulatory environment

2005· article· en· W2132574271 on OpenAlexaffabout
Anderson Chuck, Philip Jacobs, J. Wayne Logus, Donald St. Hilaire, Chester Chmielowiec, Alexander McEwan

Bibliographic record

VenueInternational Journal of Technology Assessment in Health Care · 2005
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsInstitute of Health EconomicsUniversity of Alberta
Fundersnot available
KeywordsMarginal costPositron emission tomographyNuclear medicineOperating expenseCost estimateOperating costCapital costVariable costOperations managementBusinessMedicineEconomicsFinanceAccounting

Abstract

fetched live from OpenAlex

OBJECTIVES: Cost studies of positron emission tomography (PET) imaging are important for resource and operational planning; the most relevant cost analysis in this regard is the marginal cost. Operating within a regulatory environment can add considerably to the costs of providing PET services. Previously published research has not examined the marginal cost structure of PET nor have they described the implications of regulatory compliance to operational costs. The purpose of this study was to conduct a comprehensive cost estimation of PET imaging with 18F-fluorodeoxyglucose (18F-FDG) to better identify the fixed and variable cost components, the marginal cost structure, and the added costs of satisfying regulatory requirements. METHODS: Financial data on capital and operating expenses were collected for the PET center at the Cross Cancer Institute in Edmonton, Alberta, Canada. RESULTS: The total per-service cost for clinical operations ranged between $7,869 (400 annual scans) and $1,231 (3,200 annual scans). The marginal cost for the center remained steady as volume increased up to the throughput capacity. CONCLUSIONS: Results indicate that economies from increased volumes did not arise. Regulatory requirements added significant costs to operating an 18F-FDG-PET center.

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 score0.168
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.010
GPT teacher head0.374
Teacher spread0.363 · 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

Citations21
Published2005
Admission routes2
Has abstractyes

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