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Record W2085042103 · doi:10.1259/dmfr/22131776

Variation in costs of cone beam CT examinations among healthcare systems

2012· article· en· W2085042103 on OpenAlexaff
Helena Christell, Stephen Birch, Mihaela Hedeșiu, Keith Horner, Deimantė Ivanauskaitė, Olivia Nackaerts, Madeleine Rohlin, Christina Lindh

Bibliographic record

VenueDentomaxillofacial Radiology · 2012
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIndirect costsValuation (finance)Cone beam computed tomographyHealth careVariable costCost–benefit analysisCapital costIncentiveMedicineCapital equipmentOperations managementHealthcare systemBusinessEconomicsComputed tomographyIndustrial organizationRadiologyFinanceAccountingMicroeconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: To analyse the costs of cone beam CT (CBCT) in different healthcare systems for patients with different clinical conditions. METHODS: Costs were calculated for CBCT performed in Cluj (Romania), Leuven (Belgium), Malmö (Sweden) and Vilnius (Lithuania) on patients with (i) a maxillary canine with eruption disturbance, (ii) an area with tooth loss prior to implant treatment or (iii) a lower wisdom tooth planned for removal. The costs were calculated using an approach based on the identification, measurement and valuation of all resources used in the delivery of the service that combined direct costs (capital equipment, accommodation, labour) with indirect costs (patients' and accompanying persons' time, "out of pocket" costs for examination fee and visits). RESULTS: The estimates for direct and indirect costs varied among the healthcare systems, being highest in Malmö and lowest in Leuven. Variation in direct costs was mainly owing to different capital costs for the CBCT equipment arising from differences in purchase prices (range €148 000-227 000). Variation in indirect costs were mainly owing to examination fees (range €0-102.02). CONCLUSIONS: Cost analysis provides an important input for economic evaluations of diagnostic methods in different healthcare systems and for planning of service delivery. Additionally, it enables decision-makers to separate variations in costs between systems into those due to external influences and those due to policy decisions. A cost evaluation of a dental radiographic method cannot be generalized from one healthcare system to another, but must take into account these specific circumstances.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

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

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 head0.017
GPT teacher head0.285
Teacher spread0.267 · 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

Citations30
Published2012
Admission routes1
Has abstractyes

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