Determinants of durable humoral and T cell immunity in myeloma patients following <scp>COVID</scp>‐19 vaccination
Bibliographic record
Abstract
OBJECTIVE: To describe determinants of persisting humoral and cellular immune response to the second COVID-19 vaccination among patients with myeloma. METHODS: This is a prospective, observational study utilising the RUDYstudy.org platform. Participants reported their second and third COVID-19 vaccination dates. Myeloma patients had an Anti-S antibody level sample taken at least 21 days after their second vaccination and a repeat sample before their third vaccination. RESULTS: 60 patients provided samples at least 3 weeks (median 57.5 days) after their second vaccination and before their third vaccination (median 176.0 days after second vaccine dose). Low Anti-S antibody levels (<50 IU/mL) doubled during this interval (p = .023) and, in the 47 participants with T-spot data, there was a 25% increase negative T-spot tests (p = .008). Low anti-S antibody levels prior to the third vaccination were predicted by lower Anti-S antibody level and negative T-spot status after the second vaccine. Independent determinants of a negative T-spot included increasing age, previous COVID infection, high CD4 count and lower percentage change in Anti-S antibody levels. CONCLUSIONS: Negative T-spot results predict low Anti-S antibody levels (<50 IU/mL) following a second COVID-19 vaccination and a number of biomarkers predict T cell responses in myeloma patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".