Common variable immunodeficiency (CVID) with granulomatous interstitial lung disease (GLILD) and SARS COVID-19 infection: case report and review of literature
Bibliographic record
Abstract
BACKGROUND: We present a case of CVID complicated by granulomatous interstitial lung disease (GLILD). This patient clinical course was further complicated by COVID-19 infection. This is only the 2nd known case report of COVID 19 in CVID with GLILD. The clinical course and outcome of COVID 19 infection with common variable immunodeficiency (CVID) and GLILD is not well known. CASE PRESENTATION: Our patient met the clinical features of CVID secondary to low IgG/IgA, recurrent infections, and failure to respond to pneumococcal vaccination. He was treated with monthly maintenance IVIG therapy. Our patient also was diagnosed with co-existing GLILD that despite IVIG treatment was progressing. The patient needed to be started on Rituxan and Mycophenolate mofetil to achieve control but unfortunately became infected with COVID19 delaying his treatment for GLILD. Our patient only suffered from mild COVID 19 infection and was able to make antibodies to this. We believe severe infection was avoided as his CVID was well controlled with IVIG therapy despite progression of his granulomatous interstitial lung disease. CONCLUSION: In conclusion, our patient with CVID with co-existing biopsy proven granulomatous interstitial lung disease despite being very high risk for severe COVID 19 infections only had mild infection. This was believed to be due to well controlled CVID with IVIG therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".