Therapeutic Vaccines for Follicular Lymphoma: A Systematic Review
Bibliographic record
Abstract
(1) Background: We aimed to estimate the pooled effectiveness and safety of vaccination in follicular lymphoma (FL) and discuss implications for immunotherapy development. (2) Methods: We included randomized trials (RCTs) of therapeutic vaccines in patients with FL. Progression-free survival (PFS) was the primary outcome. We searched databases (PubMed, Embase, Scopus, Web of Science Core, medRxiv) and registries (PROSPERO, CENTRAL, ClinicalTrials.gov, EuCTR, WHO ICTRP) and conducted online, citation, and manual searches. We assessed risks of bias across outcomes using RoB 2.0 and across studies using ROB-ME and a contour-enhanced funnel plot. (3) Results: Three RCTs were included (813 patients, both previously treated and untreated). Patients with a complete or partial response after chemotherapy were randomized to either a patient-specific recombinant idiotype keyhole limpet hemocyanin (Id-KLH) vaccine plus granulocyte-macrophage colony-stimulating factor (GM-CSF) or placebo immunotherapy (KLH + GM-CSF). Meta-analyses showed that PFS was worse with the vaccine, but not significantly: hazard ratio, 1.09 (95% CI 0.91-1.30). The GRADE certainty of evidence was moderate. Adverse event data were mixed. (4) Conclusions: We are moderately certain that Id-KLH results in little to no difference in PFS in FL. (5) Funding: Russian Science Foundation grant #22-25-00516. (6) Registration: PROSPERO CRD42023457528.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".