Uncovering the Hidden: A Study on Incidental Findings on CBCT Scans Leading to External Referrals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it