Variation in costs of cone beam CT examinations among healthcare systems
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".