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Record W2004184227 · doi:10.1115/imece2014-37120

High-Intensity Targeted Cavitation as a More Efficient and Safer Approach to Treat Kidney Stones

2014· article· en· W2004184227 on OpenAlexaff
Steven Dion, Louis-Philippe Riel, Michael Sourial, Martin Brouillette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCavitationSAFERShock waveIntensity (physics)Kidney stonesShock (circulatory)Computer scienceKidneyBiomedical engineeringAcousticsMedicineEngineeringSurgeryAerospace engineeringOpticsRadiologyPhysicsComputer security

Abstract

fetched live from OpenAlex

An apparatus to provide a safer and more efficient non-invasive treatment of kidney stones is under development. The proposed non-invasive alternative is to produce a tightly focused high-intensity cavitation cloud right at the stone; the cloud being electronically steerable in real time to compensate for the respiratory movements which would significantly reduce the exposition of healthy tissues to damaging shock waves. The piloted cloud is produced by 19 independent novel shock wave generators that are geometrically oriented towards a single focal point. The real-time steering is accomplished by applying different emission delays between the shock wave generators. The steering capability of the 19-channel prototype was monitored in vitro using a pressure sensor and kidney stone analogs. Promising tests were also conducted on ex-vivo pigs to measure the erosion rate of implanted artificial kidney stones.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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