Ability of Orthodontists to Detect, Interpret and Propose Management Strategies for Incidental Findings on Pre‐Treatment Panoramic Radiographs
Bibliographic record
Abstract
OBJECTIVES: Radiographs are routinely acquired for orthodontic evaluation, and incidental findings (IFs) may be detected early as part of this routine care. This study aimed to assess the prevalence of IFs on panoramic radiographs taken for orthodontic assessment and evaluate the ability of orthodontists to detect, interpret and recommend management for IFs. MATERIALS AND METHODS: A retrospective analysis of 1756 patients aged 7-21 with a panoramic image taken for orthodontic evaluation was performed. IFs were categorised by anatomic region and assigned a significance level based on a custom-made risk assessment tool. Twelve selected images were evaluated by 30 orthodontists via a standardised online survey. Participants were tasked with reviewing each image, providing a radiological interpretation, and indicating whether a referral would be needed prior to commencing orthodontic treatment. The responses were scored against those of an expert oral and maxillofacial radiologist (OMFR). Inter-observer agreement was evaluated by computing the Cohen's K. RESULTS: The prevalence of IFs was 30.8%. The most common findings were impacted teeth (17.7%), hypodontia (14.9%) and dense bone islands (14.7%). After applying the risk assessment tool, 35% of the findings were considered highly significant. The overall agreement between the orthodontists and OMFR was k = 0.32 (95% CI = 0.30-0.34). The agreement for location was 0.52 (95% CI = 0.47-0.58), whereas it was k = 0.45 (95% CI = 0.40-0.50) for diagnosis and k = 0.19 (95% CI = 0.13-0.24) for referral need. CONCLUSIONS: Orthodontists may benefit from additional education and training focused on interpretation and management of IFs on panoramic radiographs.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".