Uncovering the Hidden: A Study on Incidental Findings on CBCT Scans Leading to External Referrals
Bibliographic record
Abstract
OBJECTIVES: This project aims to determine the prevalence of cone-beam computed tomography (CBCT) findings requiring referral. Additionally, the goal is to establish a reference standard protocol for incidental findings, outlining indications for further investigation and management protocol. METHODS: Patients records from the Advanced Imaging Centre at the School of Dentistry, University of Alberta, underwent systematic examination to identify CBCT incidental findings. Radiographic findings requiring referral were categorised into 8 anatomic zones. Analysis assessed prevalence and a management protocol was developed for significant findings. Inferential analyses were conducted to determine the frequency and prevalence of specific findings requiring further investigation. RESULTS: A total of 1260 CBCT interpretive reports were analysed. The most prevalent radiographic findings outside the areas of interest were found in the cervical vertebrae (18%), followed by the sinuses (15%), temporomandibular joints (8%), jaw lesions (7%), airway (5%), teeth (5%), soft tissue calcifications (5%), and other (1%). CONCLUSIONS: Findings most commonly requiring external referral included carotid atheroma (2.7%), cervical vertebrae osteoarthritis (0.97%), jaw lesions (0.86%), adenoid and/or tonsillar hypertrophy (0.86%), and paranasal sinus pathology (0.73%). Increased medicolegal awareness and practitioner knowledge contribute to the rising number of CBCT-identified radiographic findings outside the area of concern. The study addresses the debate on reporting all CBCT/radiographic findings by exploring their prevalence and providing protocols. These guidelines assist dentists in identification, decision-making, and referral processes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".