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Record W4402864951 · doi:10.1080/20415990.2024.2389032

Exploring delivery systems for targeted nanotechnology-based gene therapy in the inner ear

2024· review· en· W4402864951 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTherapeutic Delivery · 2024
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsCentre for Drug Research and Development
FundersCurtin University of Technology
KeywordsInner earNanotechnologyGenetic enhancementGene deliveryTargeted therapyMedicineMaterials scienceBiologyGeneAnatomyInternal medicineGenetics

Abstract

fetched live from OpenAlex

Hearing loss places a significant burden on our aging population. However, there has only been limited progress in developing therapeutic techniques to effectively mediate this condition. This review will outline several of the most commonly utilized practices for the treatment of sensorineural hearing loss before exploring more novel techniques currently being investigated via both in vitro and in vivo research. This review will place particular emphasis on novel gene-delivery technologies. Primarily, it will focus on techniques used to deliver genes that have been shown to encourage the proliferation and differentiation of sensory cells within the inner ear and how these technologies may be translated into providing clinically useful results for patients.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.314
GPT teacher head0.336
Teacher spread0.022 · 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