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Record W4200203778 · doi:10.14740/jocmr4597

Intravascular Ultrasound Can Be Used to Locate Nerves, but not Confirm Ablation, During Renal Sympathetic Denervation

2021· article· en· W4200203778 on OpenAlexvenueno aff
Keisuke Okamura, Shunsuke Satou, Yusuke Kato, Yusuke Kogata, Masatoshi Matsushima, Kazuyuki Shirai, Hidenori Urata

Bibliographic record

VenueJournal of Clinical Medicine Research · 2021
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntravascular ultrasoundRenal sympathetic denervationAblationEchogenicityDenervationRenal arteryRadiologyCatheter ablationAnatomyUltrasoundKidneyCardiologyInternal medicineBlood pressureResistant hypertension

Abstract

fetched live from OpenAlex

BACKGROUND: No methods exist for confirming nerve ablation in catheter-based renal sympathetic denervation (RDN). METHODS: We investigated the feasibility of using intravascular ultrasound (IVUS) to locate nerves and observe nerve integrity changes during RDN in a pig. To confirm our observations, we used post-RDN histological sections matched anatomically to the IVUS images. RESULTS: IVUS revealed multiple hypoechoic structures along the renal artery, whose locations matched those of nerves in the histological sections. Nerves clustered near the junction between the renal artery and vein. Histology confirmed necrosis of nerve bundles at RDN ablation sites, but no changes in echogenicity were observed using IVUS. CONCLUSIONS: Although IVUS cannot currently be used to confirm ablation during RDN, it clearly reveals some clusters of renal sympathetic nerves. It remains to be demonstrated how IVUS can guide RDN devices and potentially improve ablation success.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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

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 head0.251
GPT teacher head0.480
Teacher spread0.229 · 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 designObservational
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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Medicine Research→Same topicKidney Stones and Urolithiasis Treatments→French-language works237,207→