Health outcomes following COVID-19 infection and vaccination in hereditary hemorrhagic telangiectasia
Bibliographic record
Abstract
BACKGROUND: There has been concern that individuals living with Hereditary Hemorrhagic Telangiectasia (HHT) could be at higher risk for poor outcomes if infected with SARS-CoV2, the virus that causes COVID-19 disease. As literature is lacking on outcomes on COVID-19 infection and vaccination in HHT, the objectives of this study were to determine and assess outcomes in HHT, as well as quantify vaccination rates and vaccination side effects in a large cohort of individuals with HHT. METHOD: Individuals previously recruited to OUR HHT Registry at St. Michael's Hospital, Toronto were contacted for participation in this study. Data were collected during annual assessment through a series of questionnaires asking specifically about HHT complications, treatments, and symptom management, along with COVID infection and vaccination data. RESULTS: We attempted to contact all 262 subjects recruited to the registry. Of these, 215 (82.1%) responded at least once regarding COVID-19 related inquiries between April 2020 and August 2022, and these individuals formed our study sample. Forty-nine COVID-19 infections were reported in 47/215 (21.9%) individuals. Among 47 patients with recorded COVID-19 infection, 2/47 (4.3%) required urgent care and 7/47 (14.9%) were hospitalized following infection. Of the 7 individuals who were hospitalized, 3 (42.9%) required new supplemental oxygen. Zero deaths were reported due to COVID-19 infection. COVID vaccination history was available in 147/215 (68.4%). Of these, 135/147 (91.8%) of individuals reported vaccination and side effects were mild. DISCUSSION: While our sample population is much like the general HHT population with regards to gender, HHT symptoms, and genetics, study limitations including survivor bias, lack of vaccine effectiveness assessment, and participant reported data should be acknowledged. CONCLUSION: Our results suggest that HHT patients are not at higher risk of severe infection with COVID-19 compared to the general population. Vaccination rates are high with only mild side effects being observed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".