Therapeutic vaccines for follicular lymphoma: a systematic review protocol
Bibliographic record
Abstract
BACKGROUNDObjectives: In this review we focus on the results of clinical trials of vaccination in follicular lymphoma, and discuss potential strategies to enhance the efficacy of immunotherapy in the future.METHODSEligibility criteria: We included any clinical randomized controlled trials of therapeutic antitumor vaccines in patients with histologically confirmed follicular lymphoma. Progression-free survival is the primary outcome.Information sources: We searched PubMed, Embase (through Ovid), Scopus, available Web of Science databases, and relevant and available study registries (PROSPERO, Cochrane CENTRAL, ClinicalTrials.gov through their own interfaces, and others through WHO International Clinical Trials Registry Platform Search Portal). We did not specifically search preprint servers due to exporting issues, but some of those were searchable through Ovid Embase. We also manually reviewed included reports for relevant references and searched for additional reports of included studies via Google and trial registers.Risk of bias: We will assess risks of bias across outcomes using RoB 2.0. We will also assess risks of publication bias across studies and bias due to missing evidence in a synthesis (ROB-ME).Synthesis of results: We will conduct fixed-effects model meta-analyses if patient and intervention characteristics are homogenous across included studies. If pooling is clinically inappropriate due to a mix of newly diagnosed with relapsed or refractory lymphoma or autologous and allogenic vaccines, we will summarize studies descriptively. Finally, we will assess the certainty of the evidence and produce GRADE Summary of Findings tables.OTHERFunding: This research was supported by the Russian Science Foundation under grant # 22-25-00516.Registration: This review will be registered in PROSPERO – please follow the project repository for updates: https://doi.org/10.17605/OSF.IO/KBZFWKey words: follicular lymphoma, non-Hodgkin lymphoma, vaccines, vaccination.License: This document is licensed under CC BY-SA 4.0.
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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 | 0.057 | 0.060 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.092 | 0.012 |
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".