Salivary gland 18F-FDG-PET/CT uptake patterns in Sjögren's syndrome and giant cell arteritis patients
Bibliographic record
Abstract
OBJECTIVES: Wide variety in salivary gland 18F-FDG-uptake is observed in the general population. A general consensus about the usefulness of 18F-FDG-PET/CT to detect salivary gland inflammatory conditions, such as in primary Sjögren's syndrome (pSS), is not yet clear. This study aimed to investigate whether there are differences in uptake of 18F-FDG in salivary glands among two autoimmune groups [pSS, giant cell arteritis (GCA)] and a non-autoimmune group (lung cancer). METHODS: PSS patients aged ≥50 years who underwent 18F-FDG-PET/CT were included and age-matched with GCA patients and a non-autoimmune control group (lung cancer patients). Scans were visually evaluated and quantitative analysis was performed by measuring standardised uptake values (SUV) within salivary glands and lacrimal glands. For GCA patients, arteries in the vicinity of the parotid and submandibular gland were assessed for positivity. RESULTS: PSS patients did not show increased 18F-FDG-uptake in the parotid or submandibular gland, compared to the other two groups. For the tubarial gland, significantly higher SUVmax was found in the pSS patient group. Interestingly, GCA patients had significantly higher SUVmax in the submandibular gland than the other two groups. Visual 18F-FDG-positivity of cranial arteries related to the parotid and submandibular glands was associated with significantly higher SUVmax in salivary glands of GCA patients. CONCLUSIONS: Although 18F-FDG-uptake was not increased in parotid and submandibular glands of pSS patients, increased 18F-FDG-uptake in tubarial glands of pSS patients might indicate a role for these glands in pSS. Furthermore, parotid and submandibular glands may be affected by local vasculitis in GCA.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".