Positioning and preparation errors impacting dental panoramic radiographs in patients with mixed dentition
Bibliographic record
Abstract
Purpose: This study aimed to evaluate the quality of clinically indicated digital dental panoramic radiographs (DPRs) of children with mixed dentition. Despite the likely widespread use of this modality, recent research detailing errors on DPRs is scarce. Materials and Methods: A consecutive case series was performed, including 178 DPRs from patients aged 6 to 12 years. Each DPR was reviewed for 10 distinct errors. The findings were analyzed to identify potential solutions. Results: Nearly three-quarters of the DPRs contained multiple errors. Linear regression analysis indicated that the number of errors decreased with increasing patient age; however, this trend was not statistically significant. Notably, 3 groups of errors (2 errors each) frequently appeared together on the same DPR. When similar errors were grouped, the error incidence decreased significantly with age. Both leftward head tilting and rightward head rotation were observed, likely attributable to the design of the DPR room and the door location. The inter-rater and intra-rater reliability agreements were deemed "substantial" or "almost perfect, beyond chance" for the detection of most errors, particularly the most frequent types, which involved the "chin," "tongue," and "lips-open" positions. Conclusion: As a pediatric patient ages, the number of DPR errors decreases. The results suggest several pre-exposure strategies that could reduce the error rate. These include, monitoring for a "lips-open" position as an indicator of a potential "tongue" error (occluding the palate-glossal space), and implementing dry runs. Asymmetries observed on DPR must be documented and should prompt re-examination, as they may be genuine.
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 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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".