Giant Cell Arteritis and COVID-19: Similarities and Discriminators. A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To identify shared and distinct features of giant cell arteritis (GCA) and coronavirus disease 2019(COVID-19) to reduce diagnostic errors that could cause delays in correct treatment. METHODS: Two systematic literature reviews determined the frequency of clinical features of GCA and COVID-19 in published reports. Frequencies in each disease were summarized using medians and ranges. RESULTS: Headache was common in GCA but was also observed in COVID-19 (GCA 66%, COVID-19 10%). Jaw claudication or visual loss (43% and 26% in GCA, respectively) generally were not reported in COVID-19. Both diseases featured fatigue (GCA 38%, COVID-19 43%) and elevated inflammatory markers (C-reactive protein [CRP] elevated in 100% of GCA, 66% of COVID-19), but platelet count was elevated in 47% of GCA but only 4% of COVID-19 cases. Cough and fever were commonly reported in COVID-19 and less frequently in GCA (cough, 63% for COVID-19 vs 12% for GCA; fever, 83% for COVID-19 vs 27% for GCA). Gastrointestinal upset was occasionally reported in COVID-19 (8%), rarely in GCA (4%). Lymphopenia was more common in COVID-19 than GCA (53% in COVID-19, 2% in GCA). Alteration of smell and taste have been described in GCA but their frequency is unclear. CONCLUSION: Overlapping features of GCA and COVID-19 include headache, fever, elevated CRP and cough. Jaw claudication, visual loss, platelet count and lymphocyte count may be more discriminatory. Physicians should be aware of the possibility of diagnostic confusion. We have designed a simple checklist to aid evidence-based evaluation of patients with suspected GCA.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".