Clinical Relevance and Prognostic Significance of Isolated Angiitis of the Vasa Vasorum in Temporal Artery Biopsies
Bibliographic record
Abstract
BACKGROUND: Temporal arteritis (TA) is the most common vasculitis over age 50. Untreated, many patients will suffer blindness or stroke. Gold standard diagnosis is achieved by temporal artery biopsy. The aim of this study was to investigate the relevance of small vessel inflammation. METHODS: Our dataset was comprised of 72 temporal artery biopsies subjected to a blinded uniform re-examination paired with clinical data including demographics, history, physical examination and laboratory findings. Documented pathology variables included the presence or absence of TA, angiitis of vasa vasorum (AVV) and inflammation of small peri-adventitial vessels (small vessel vasculitis, SVV). RESULTS: Clinical and pathological variables were subjected to multivariate analysis. In brief, 25% of cases were identified as TA, 20% as isolated AVV, 7% as isolated SVV and 5% as mixed isolated AVV/SVV, while 43% had no inflammation (NI). All cases of TA were accompanied by small vessel inflammation: 95% exhibited AVV with or without SVV, and 5% exhibited SVV alone, demonstrating a strong association between TA and small vessel inflammation. Of the 24 cases with isolated AVV/SVV, 26% received a clinical diagnosis of TA within one year in comparison to 13% of cases that had NI. Furthermore, isolated AVV/SVV was identified in 25% of patients with a high clinical probability for TA, 60% of whom acquired a diagnosis of TA on clinical grounds within one year of follow-up. CONCLUSIONS: Our findings suggest that isolated AVV/SVV identifies a subgroup of patients with a higher risk of harboring or developing TA.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".