Case series of COVID-19 outcomes in adult patients with inborn errors of immunity
Bibliographic record
Abstract
Background: Since the onset of the COVID-19 pandemic, a main challenge for clinicians and public health decision-makers has revolved around risk stratification in vulnerable populations, in particular individuals with inborn errors of immunity (IEI). However, available reports of the clinical course of COVID-19 in patients with IEI show wide variability, from a complete lack of symptoms to severe and complicated disease. Objective: To present the clinical features and outcomes of SARS-CoV-2 infection in adult patients with IEI. Methods: We performed a retrospective chart review documenting patient characteristics and clinical course of SARS-CoV-2 infection between December 2021 and July 2022. Results: Ten adult patients with IEI followed in our center were diagnosed with COVID-19, as determined by RT-PCR or rapid antigen testing. IEI in this cohort included those with humoral and combined immunodeficiencies, as well as phagocytic defects. An underlying lung comorbidity was identified in 3 patients. Symptoms were mostly mild and self-limiting, and no severe outcomes, complications, or mortality were noted in this study. Conclusions: We suggest that patients affected by a wide range of both humoral and combined IEI may demonstrate resilience, while highlighting the possible protective effects of vaccination and immunoglobulin replacement in this population. Statement of Novelty: We report on the mild COVID-19 clinical course of 10 adults with IEI.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".