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Record W2134805485 · doi:10.7759/cureus.134

Cost-minimization analysis: should partial breast irradiation be utilized over whole breast irradiation assuming equivalent clinical outcomes?

2013· article· en· W2134805485 on OpenAlexafffund
Martin Leung, Michael Lock, Alexander V. Louie, George Rodrigues, David D’Souza, Robert Dinniwell, Rob Barnett

Bibliographic record

VenueCureus · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsWestern University
FundersMach-Gaensslen Foundation of Canada
KeywordsMedicineBreast cancerCost-minimization analysisActivity-based costingWorkloadRadiation therapyMedical physicsTotal costCancerOperations managementNuclear medicineSurgeryInternal medicineComputer scienceBusinessAccounting

Abstract

fetched live from OpenAlex

Introduction: External beam partial breast irradiation (EB-PBI) is being used more frequently as an alternative to whole breast irradiation (WBI) in the adjuvant treatment of early-stage breast cancer. Breast cancer represents a substantial proportion of the workload for cancer centres; therefore, EB-PBI represents a possible alternative treatment of equal effectiveness that can have a significant impact on costs and patient throughput. However, planning this therapy requires increased quality assurance and resource allocation. Therefore, a cost minimization analysis was performed to compare WBI versus EB-PBI. Based on this study, recommendations on appropriate resource allocation and cost of resources at each step of planning can be made to maximize cost efficiency. Materials and Methods: Cost minimization requires a detailed determination of resource utilization for each of the two treatments. Activity-based costing was used to create a model of radiotherapy costs. A process map was developed that separated the management of patients into differentiated quantifiable units (dosimetry, QA, active treatment, other preparatory work). Time, labour costs, and capital costs were measured using interviews and validated with timed analyses. The perspective of the analysis was that of the hospital budget at a comprehensive cancer clinic in Canada. Thus, personal patient costs and radiation oncologist labour costs were not included in the analysis as these are not funded by the hospital budget. The WBI regimen was 50.0Gy in 2.0Gy fractions, taking place over five weeks. The EB-PBI was 38.5Gy in 3.85Gy fractions twice per day for five days. The two treatment arms are considered to have equivalent clinical outcomes. Results: The total costs per patient for WBI and EB-PBI were 1346.20 Canadian dollars (2012) and 1128.70 Canadian dollars, respectively. The capital costs per patient for WBI and EB-PBI were 937.50 Canadian dollars and 721.88 Canadian dollars, respectively. Labour costs accounted for 30% of WBI and 36% of EB-PBI. EB-PBI was 19% less expensive than WBI. Per patient, this is a cost difference of $217.50, or savings of $21,562.50 based on the department workload of 100 breast cancer patients per linear particle accelerator per year. The majority of the cost differences arose from both capital and labour costs needed for the extra fractions per patient required for WBI. Conclusions: EB-PBI significantly minimizes costs in the treatment of early-stage breast cancer relative to WBI. These results may be utilized by other institutions with other similar health care systems when executing decisions regarding resource allocation in the context of early-stage breast cancer treatment. Costs can be adjusted for each activity within the model. In addition, changes in operating parameters can be adjusted allowing other centres to determine detailed cost impacts specific to their own centre. The model can also be applied to different disease treatment methods.

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)0.008
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.070
GPT teacher head0.369
Teacher spread0.298 · 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 designSimulation or modeling
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

Citations1
Published2013
Admission routes2
Has abstractyes

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