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Record W4386120672 · doi:10.31219/osf.io/evj59

Therapeutic vaccines for follicular lymphoma: a systematic review protocol

2023· review· en· W4386120672 on OpenAlexfundno aff
Pavel Zhelnov, Andrei Suponin, A. A. Potanin, Aleksandr Lomazov, Kseniia Vladimirova, Kirill V. Lepik, Аlbert R. Muslimov

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsnot available
FundersRussian Science FoundationUniversity of Toronto
KeywordsMedicineFollicular lymphomaClinical trialProtocol (science)MEDLINERandomized controlled trialCINAHLOncologyFamily medicineInternal medicineLymphomaAlternative medicinePsychological interventionPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.057
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.092
Threshold uncertainty score0.308

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.060
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0150.015
Bibliometrics0.0140.011
Science and technology studies0.0030.004
Scholarly communication0.0060.007
Open science0.0050.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0920.012

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.

Opus teacher head0.095
GPT teacher head0.425
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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