Dental Students’ Interpretations of Digital Panoramic Radiographs on Completely Edentate Patients
Bibliographic record
Abstract
The ability of dental students to interpret digital panoramic radiographs (PANs) of edentulous patients has not been documented. The aim of this retrospective study was to compare the ability of second-year (D2) dental students with that of third- and fourth-year (D3-D4) dental students to interpret and identify positional errors in digital PANs obtained from patients with complete edentulism. A total of 169 digital PANs from edentulous patients were assessed by D2 (n=84) and D3-D4 (n=85) dental students at one Canadian dental school. The correctness of the students' interpretations was determined by comparison to a gold standard established by assessments of the same PANs by two experts (a graduate student in prosthodontics and an oral and maxillofacial radiologist). Data collected were from September 1, 2006, when digital radiography was implemented at the university, to December 31, 2012. Nearly all (95%) of the PANs were acceptable diagnostically despite a high proportion (92%) of positional errors detected. A total of 301 positional errors were identified in the sample. The D2 students identified significantly more (p=0.002) positional errors than the D3-D4 students. There was no significant difference (p=0.059) in the distribution of radiographic interpretation errors between the two student groups when compared to the gold standard. Overall, the category of extragnathic findings had the highest number of false negatives (43) reported. In this study, dental students interpreted digital PANs of edentulous patients satisfactorily, but they were more adept at identifying radiographic findings compared to positional errors. Students should be reminded to examine the entire radiograph thoroughly to ensure extragnathic findings are not missed and to recognize and report patient positional errors.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".