Clinical Course and Management of a Consecutive Series of Patients with “Healed Temporal Arteritis”
Bibliographic record
Abstract
OBJECTIVE: To describe the clinical course and management of patients with a pathologic diagnosis of "healed" giant cell arteritis (GCA), and to determine whether previously published histological descriptions of healed arteritis can identify patients with a greater likelihood of clinically significant arteritis. METHODS: All temporal artery biopsy reports between 1994 and 2003 were examined for a diagnosis of "healed arteritis." Two rheumatologists abstracted the medical record for presenting features, physical findings, comorbid conditions, and data on treatment and outcomes. One pathologist, blinded to the clinical data, reviewed all specimens and reinterpreted the biopsies according to published histological descriptions of healed arteritis. RESULTS: Forty-seven patients with an initial pathologic diagnosis of healed arteritis were identified. In 54% of these patients, corticosteroid therapy did not change after the diagnosis of healed arteritis was documented in the pathology report. Seventy percent were ultimately treated with no corticosteroids or low-moderate corticosteroid regimens. Only 32% of the initial cases were confirmed upon review of the biopsies using standardized histological criteria. Patients with confirmed healed arteritis were more likely to have a documented history of polymyalgia rheumatica/GCA and a longer duration of corticosteroid treatment before biopsy. These patients were not more likely to have adverse outcomes. CONCLUSION: In this case series, the diagnosis of healed arteritis had little effect on treatment decisions. In most cases, the initial pathologic diagnosis of healed arteritis was not confirmed when biopsies were reviewed by a single pathologist using uniform histological criteria.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".