Digital Intraoral Imaging Re‐Exposure Rates of Dental Students
Bibliographic record
Abstract
A guiding principle of radiation safety is ensuring that radiation dosage is as low as possible while yielding the necessary diagnostic information. Intraoral images taken with conventional dental film have a higher re-exposure rate when taken by dental students compared to experienced staff. The aim of this study was to examine the prevalence of and reasons for re-exposure of digital intraoral images taken by third- and fourth-year dental students in a dental school clinic. At one dental school in Canada, the total number of intraoral images taken by third- and fourth-year dental students, re-exposures, and error descriptions were extracted from patient clinical records for an eight-month period (September 2015 to April 2016). The data were categorized to distinguish between digital images taken with solid-state sensors or photostimulable phosphor plates (PSP). The results showed that 9,397 intraoral images were made, and 1,064 required re-exposure. The most common error requiring re-exposure for bitewing images was an error in placement of the receptor too far mesially or distally (29% for sensors and 18% for PSP). The most common error requiring re-exposure for periapical images was inadequate capture of the periapical area (37% for sensors and 6% for PSP). A retake rate of 11% was calculated, and the common technique errors causing image deficiencies were identified. Educational intervention can now be specifically designed to reduce the retake rate and radiation dose for future patients.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".