A systematic review on incidental findings in cone beam computed tomography (CBCT) scans
Bibliographic record
Abstract
OBJECTIVES: Cone beam CT's use (CBCT) in dentistry is increasing. Incidental findings (IFs: discoveries unrelated to the original purpose of the scan), are frequently found as a result of a large field of view. The aim of the systematic review is to analyze present literature on IFs using CBCT. METHODS AND MATERIALS: The authors searched online databases of studies and assessed the prevalence of IFs among patients undergoing head and neck CBCT scans. STROBE criteria was used to evaluate the quality of the studies. RESULTS: The original search retrieved 509 abstracts of which only 10 articles met the inclusion criteria. The sample size varied between 90 and 1000 participants. The frequency of IFs of the selected articles were 24.6-94.3%. The most common non-threatening IFs were found in the airway, such as mucous retention cyst (55.1%) and sinusitis (41.7%). Other non-threatening IFs were soft tissue calcifications such as calcified stylohyloid ligament (26.7%), calcified pineal gland (19.2%), and tonsillolith (14.3%). Threatening IFs were rare findings (1.4%). Three articles reported incidental carotid artery calcifications with a prevalence of 5.7-11.6%. Pathological findings were not common between the articles, but still relevant (2.6%). The studies had a risk of bias varying from moderate to low. CONCLUSIONS: There is a high frequency of IFs, yet not all of them require immediate medical attention. The low prevalence of threatening IFs emphasizes that CBCT should not be considered a substitution for conventional radiographs, but when used, the scans should be evaluated by a maxillofacial radiologist.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".