An Evaluation of Cone‐Beam Computed Tomography Use in Postgraduate Orthodontic Programs in the United States and Canada
Bibliographic record
Abstract
The purpose of this study was to evaluate the use of cone-beam computed tomography (CBCT) in postgraduate orthodontic residency programs. An anonymous electronic survey was sent to the program director/chair of each of the sixty-nine United States and Canadian postgraduate orthodontic programs, with thirty-six (52.2 percent) of these programs responding. Overall, 83.3 percent of programs reported having access to a CBCT scanner, while 73.3 percent reported regular usage. The vast majority (81.8 percent) used CBCT mainly for specific diagnostic purposes, while 18.2 percent (n=4) used CBCT as a diagnostic tool for every patient. Orthodontic residents received both didactic and practical (hands-on) training or solely didactic training in 59.1 percent and 31.8 percent of programs, respectively. Operation of the CBCT scanner was the responsibility of radiology technicians (54.4 percent), both radiology technicians and orthodontic residents (31.8 percent), and orthodontic residents alone (13.6 percent). Interpretation of CBCT results was the responsibility of a radiologist in 59.1 percent of programs, while residents were responsible for reading and referring abnormal findings in 31.8 percent of programs. Overall, postgraduate orthodontic program CBCT accessibility, usage, training, and interpretation were consistent in Eastern and Western regions, and most CBCT use was for specific diagnostic purposes of impacted/supernumerary teeth, craniofacial anomalies, and temporomandibular joint (TMJ) disorders.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".