The effective dose of different scanning protocols using the Sirona GALILEOS<sup>®</sup>comfort CBCT scanner
Bibliographic record
Abstract
OBJECTIVES: To determine the effective dose and CT dose index (CTDI) for a range of imaging protocols using the Sirona GALILEOS(®) Comfort CBCT scanner (Sirona Dental Systems GmbH, Bensheim, Germany). METHODS: Calibrated optically stimulated luminescence dosemeters were placed at 26 sites in the head and neck of a modified RANDO(®) phantom (The Phantom Laboratory, Greenwich, NY). Effective dose was calculated for 12 different scanning protocols. CTDI measurements were also performed to determine the dose-length product (DLP) and the ratio of effective dose to DLP for each scanning protocol. RESULTS: The effective dose for a full maxillomandibular scan at 42 mAs was 102 ± 1 μSv and remained unchanged with varying contrast and resolution settings. This compares with 71 μSv for a maxillary scan and 76 μSv for a mandibular scan with identical milliampere-seconds (mAs) at high contrast and resolution settings. CONCLUSIONS: Changes to mAs and beam collimation have a significant influence on effective dose. Effective dose and DLP vary linearly with mAs. A collimated maxillary or mandibular scan decreases effective dose by approximately 29% and 24%, respectively, as compared with a full maxillomandibular scan. Changes to contrast and resolution settings have little influence on effective dose. This study provides data for setting individualized patient exposure protocols to minimize patient dose from ionizing radiation used for diagnostic or treatment planning tasks in dentistry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".