Incidental findings in a consecutive series of digital panoramic radiographs
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine the prevalence of incidental findings (IFs) on digital dental panoramic radiographs (DPRs) of asymptomatic patients attending a general dental practice. MATERIALS AND METHODS: This was a retrospective study of 6,252 consecutive digital (photostimulatable phosphor) DPRs of patients who visited a Canadian general dental practice for a complete new patient examination. The IFs were grouped into dental-related anomalies, radiopacities and radiopacities in the jaws, changes in the shape of the condyles, and other findings in the jaws, such as tonsilloliths and mucosal antral pseudocysts. Their prevalence was determined. RESULTS: Thirty-two percent of the DPRs showed at least 1 IF. The highest prevalence was found for dental-related anomalies (29% of all DPRs), of which impacted teeth were the most prevalent finding (24% of all DPRs), followed by idiopathic osteosclerosis (6% of all DPRs). A lower prevalence was noted for tonsilloliths (3%), and the prevalence of root tips, mucosal antral pseudocysts, and anomalies in condylar shape was approximately 1% each. CONCLUSION: The observed prevalence of 32.1% for IFs of any type underscores the need for a dental practitioner to review the entire DPR when a patient presents for an initial dental examination (or check-up) or for dental hygiene. Only a single IF (a central giant cell granuloma) provoked alarm, as it was initially considered malignant. Similarly, impacted teeth and suspected cysts need careful evaluation upon discovery to determine how they may be optimally managed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.005 |
| 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 | 0.000 | 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".