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Record W1748535404 · doi:10.1186/2050-5736-3-s1-p1

An instrumented bone/soft tissue phantom designed to mimic HIFU treatments of bone

2015· article· en· W1748535404 on OpenAlexaff
Jemma Brown, Gail ter Haar

Bibliographic record

VenueJournal of Therapeutic Ultrasound · 2015
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsInstitute of Cancer Research
FundersFocused Ultrasound Foundation
KeywordsMedicineImaging phantomSoft tissueBiomedical engineeringRadiologyMedical physicsNuclear medicine

Abstract

fetched live from OpenAlex

Background/introductionClinically, HIFU exposures of bone are used to palliate pain from primary or secondary bone tumours.Such tumours weaken bone structure and render the patient susceptible to bone fracture.Ultrasound metrology for bone exposures is extremely challenging.The ultrasound beam is strongly reflected at the bone surface, with rapid surface absorption of sound entering the cortex.Pain relief is obtained when the HIFU induced temperature increase ablates the peri-osteal nerves.Standard PRF based MR thermometry, used for treatment monitoring, is inappropriate for bone.Thus, a method of determining the temperature distribution in a clinical environment is needed.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.328
Teacher spread0.297 · 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
GenreMethods

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

Citations0
Published2015
Admission routes1
Has abstractyes

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