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Record W2789350718 · doi:10.1017/s1551929500057618

Novel Developments in High-frequency Micro-Ultrasound Imaging

2006· article· en· W2789350718 on OpenAlexaff
T. M. Little

Bibliographic record

VenueMicroscopy Today · 2006
Typearticle
Languageen
FieldMedicine
TopicUltrasound Imaging and Elastography
Canadian institutionsFujiFilm VisualSonics (Canada)
Fundersnot available
KeywordsUltrasoundBlood flowMedicineBiomedical engineeringModality (human–computer interaction)Doppler imagingRadiologyUltrasound imagingMedical physicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract With the mapping of the mouse genome, the growing sophistication in transgenic sciences creating complex mouse models of disease, and the demand to study disease in vivo, there has been a corresponding increase in the demand for and development of preclinical imaging modalities. Clinical ultrasound operating in the 2-12 MHz range is a well established clinical imaging modality, accounting for more than one-third of all imaging procedures performed in North America. The simplicity, ease of use, speed, and safety of ultrasound have led to its significant role in diagnosis, treatment assessment, follow-up, and guidance of therapy in clinical applications. Ultrasound imaging is used routinely in its B-Mode imaging mode to report on soft tissue structures. It's also used in its Doppler modes for the measurement of blood velocity in fast-flowing targets such as the cardiovascular system, in slow-flowing applications such as quantifying blood flow and in vascular architectures within tumors.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.006
GPT teacher head0.246
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2006
Admission routes1
Has abstractyes

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