Global cone‐beam computed tomography adoption, usage and scan interpretation preferences of dentists and endodontists
Bibliographic record
Abstract
AIM: This study investigated the adoption of cone-beam computed tomography (CBCT) by dentists and endodontists around the world, including their preferences in endodontic CBCT usage. METHODOLOGY: An online questionnaire surveyed dental association members in Australia and New Zealand, and endodontic association members in Australia, Britain, Canada, Italy, New Zealand and the USA, about their CBCT training history, considerations in acquisition/interpretation, access to and usage of CBCT, preferred scan interpreter, and preferred endodontic scan settings. Data were analysed with Chi-squared, independent sample t-tests, Cochran's Q and McNemar's tests. RESULTS: Responses from 578 endodontic specialists or postgraduates (Group E) and 185 non-endodontic dentists (Group NE) were included. Continuing professional education (CPE) was the most common source of CBCT training (69.2%). Factors considered in CBCT acquisition/interpretation included beam hardening (75.4%), radiation exposure (61.1%) and patient movement (58.3%). Group E reported higher CBCT usage (90.8%) than Group NE (45.4%, p < .001) and greater workplace access to CBCT (81.1% vs. 25.9%, p < .001). Scans were interpreted by the respondent in most workplace scans (83.3%) and externally taken scans (60.5%); Group E were significantly more likely to interpret themselves than Group NE. Small field of view (83.6%) and high resolution (86.6%) were most preferred as settings for endodontic CBCTs; Group NE were less likely to choose these settings. There were some geographic variations within Group E. CONCLUSIONS: CBCT training was most commonly acquired via CPE. Endodontic respondents reported very high CBCT usage and access in the workplace. There are educational implications regarding CBCT limitations, appropriate applications and interpretation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".