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Record W4396668167 · doi:10.1016/j.ymthe.2024.04.036

Development of an mRNA-based therapeutic vaccine mHTV-03E2 for high-risk HPV-related malignancies

2024· article· en· W4396668167 on OpenAlexaff
Jing Wang, Qixin Wang, Ling Ma, Kai Lv, Lu Han, Yunfeng Chen, Rui Zhou, Hua Chen, Yi Wang, Tingting Zhang, Dongrong Yi, Qian Liu, Yongxin Zhang, Xiaoyu Li, Tingting Cheng, Jinming Zhang, Chunjian Huang, Yijie Dong, Weiguo Zhang, Shan Cen

Bibliographic record

VenueMolecular Therapy · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsBiotechnology Research Institute
FundersChinese Academy of Medical Sciences Initiative for Innovative MedicineNational Natural Science Foundation of ChinaNational Medical Research CouncilChinese Academy of Meteorological SciencesChinese Academy of SciencesInstitute of Biophysics, Chinese Academy of Sciences
KeywordsImmune systemAntigenImmunityVaccinationImmunologyCD8Cancer researchMedicineBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Development of an mRNA therapeutic vaccine for HPV-related malignancies; the object is a vaccine.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

It concerns development of a therapeutic cancer vaccine, not research practice.

Grok 4.5OUT
genre: other
about Canada: no
confidence: high

Therapeutic HPV mRNA vaccine development; biomedical product research, not metaresearch.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations27
Published2024
Admission routes1
Has abstractno

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