A comparative study of the diagnostic capabilities of 2D plain radiograph and 3D cone beam CT sialography
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare the diagnostic capabilities of two-dimensional sialography with a novel three-dimensional technique using cone beam CT (CBCT). METHODS: 47 subjects underwent parotid or submandibular gland sialography over a 2 year period using both plain imaging and CBCT. Both image sets were anonymized and independently reviewed by three certified oral and maxillofacial radiologists blinded to the clinical data. McNemar's χ(2) test was used to determine differences between the two modalities for feature visualization and interpretation. RESULTS: CBCT outperformed plain imaging with respect to visualization of the gland parenchyma (p < 0.001) and identification of sialoliths (p = 0.02). Plain imaging outperformed CBCT for the identification of strictures (p = 0.04); however, the negative per cent agreement ("specificity") between the two imaging modalities was 100%. Although both imaging modalities performed equally in identifying normal and abnormal sialographic examinations, CBCT demonstrated a high negative per cent agreement for normal glands and a high positive per cent agreement ("sensitivity") for abnormal glands with inflammatory changes. CONCLUSION: CBCT sialography allowed better visualization of gland parenchyma and identification of sialoliths. The high negative per cent agreement for strictures suggests that, if strictures are identified on CBCT images, then obstruction can be ruled in. Relative to plain images, the high negative per cent agreement for normal glands suggests that, if an abnormal finding is detected on CBCT images, then disease can be ruled in, and the high positive per cent agreement for glands with inflammatory changes suggests that inflammation can be ruled out if these changes are not seen on CBCT images.
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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 | 0.005 | 0.019 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".