Quantitative Assessment of Computed Tomography Energy Use and Cost Savings Through Overnight and Weekend Power Down in a Radiology Department
Bibliographic record
Abstract
Purpose: To assess energy and cost savings when a CT scanner is powered down during overnight non-operational times compared with the CT scanner left on full power or partial shutdown mode. Materials and Methods: Temporary portable power data loggers were placed on the power supply to the CT scanner to measure the energy consumption of the CT scanner in 3 power modes over 9 weeks: system ON (computer on, gantry on), computer ON (computer on/gantry off), and system shutdown (computer off/gantry off). Energy was separated into daily average consumption during normal operating hours and consumption after hours for three different day types: weekdays, Saturdays, and Sundays/Holidays. To estimate savings, the average after hours energy use per day during the system ON was compared to each of the two power saving modes. 95% confidence intervals were calculated for each mode and savings result. Results: Overnight and Sunday system shutdown compared to system ON mode is shown to save approximately 14 000 kWh over one year with a 95% confidence interval of (13 899 kWh, 14 464 kWh) as calculated by the electricity provider. Conclusion: Energy consumed by a CT scanner can be significantly reduced through system shutdown when the unit is non-operational, saving emissions and cost. In addition to cost and energy savings, this study emphasizes the importance of clinician leadership in convening interdisciplinary teams outside of usual healthcare silos to rethink how we purposefully use energy and reduce waste.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".