Response to and outcomes of the Pfizer <scp>BNT162B2</scp> vaccine in hemodialysis patients—A prospective observational study
Bibliographic record
Abstract
INTRODUCTION: COVID-19 infection is associated with high mortality among hemodialysis patients. Standard vaccine response is generally lower among these patients. The adequate antibody titer response and the outcome of COVID-19 vaccine responders versus non-responders are unknown. METHODS: Hemodialysis patients on maintenance hemodialysis who have received two doses of Pfizer BNT162B2 vaccine were studied. Antibody response was tested after 14 days of the second dose. LIAISON SARS-CoV2 S1/S2 IgG test by DiaSorin (Italy) was used to assess antibody response. Patients were followed between 3 and 7 months after vaccination for COVID-19 infection, hospitalization and death related to COVID-19. FINDINGS: A total of 138 patients received two doses of Pfizer BNT162B2 vaccine. One hundred and twenty-seven patients had adequate response to the vaccine with IgG level ≥ 15 AU/ml versus 11 patients had poor response with IgG level ≤ 15 AU/ml. The response was 92% (127/138). Patient with history of prior COVID-19 infection had higher antibody titer mean of 339 ± 113 versus 157 ± 140 for patient with no prior history of COVID-19. Seven patients in both groups had COVID-19 infection post vaccine. Among the responders, five patients had COIVD-19 infection and two were hospitalized. These two patients had lower antibody titer of 23.9 and 75.2 AU/ml. In comparison, three patients who were not hospitalized had higher antibody titer 96.3, 118, and 319 AU/ml, respectively. In the non-responders one patient was hospitalized and one death occurred with rate of infection of 18%. DISCUSSION: Seropositive patients with low antibody titer might be associated with worse outcome among responders. The ideal antibody titer level among dialysis patient is not known. Also, prior COVID-19 infection is associated with higher response to vaccine with higher antibody titer. All non-responders did not have prior COVID-19 infection. More research is required to further evaluated protective antibody titer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".