THE EFFECT OF HEALTHCARE TECHNOLOGY ON HEALTH EXPENDITURES
Bibliographic record
Abstract
This study aimed to determine the effect of Magnetic Resonance Imaging and Computed Tomography imaging services, which are within the scope of health technologies, on Health Expenditures. Panel analyses was used for the analyses. In the study, health expenditures were determined as the dependent variable, and the number of MR imaging and CT imaging within the scope of health technology as the independent variable, and 16 countries with regular data for the years 2007-2018 were included in the analysis. These countries were Australia, Belgium, Canada, Chile, The Czech Republic, Denmark, France, Germany, Iceland, Israel, Korea, Latvia, Lithuania, Luxembourg, Slovak Republic, and Slovenia. As a result of the analyses, it was determined that a one-unit increase in the number of Computed Tomography imaging increased health expenditures by 3.23 units, and a one-unit increase in the number of Magnetic Resonance imaging increased health expenditures by 21.9 units. The results of the study revealed that there was a positive and long-term relationship between health expenditures and the number of Computed Tomography and Magnetic Resonance Imaging, and there was a causal relationship in different directions between the variables. In addition, it has been determined that the number of Magnetic Resonance Imaging has increased more than the number of Computed Tomography imaging over the years, and that the number of Magnetic Resonance Imaging has a higher impact on health expenditures than Computed Tomography. When these two results were evaluated together, it is predicted that evaluating Magnetic Resonance Imaging and developing remedial activities will reduce health expenditures.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".