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Record W3006359571

Magnetic seed localization for soft tissue lesions in breast patients: Clinical effectiveness, cost-effectiveness, and guidelines

2019· article· en· W3006359571 on OpenAlexaboutno aff
Shirley S. T. Yeung, Kelly Farrah

Bibliographic record

VenueEurope PMC (PubMed Central) · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerBreast tissueRadiologyMagnetic resonance imagingMedical physicsSurgeryCancerInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Magnetic seed, also known as Magseed needle with magnetic marker system, was first approved by Health Canada in April 2014 and used to localize non-palpable breast lesions., It consists of a magnetic marker, the size of a grain of rice, which can be detected using the Sentimag® probe during surgery. The magnetic seeds can be inserted up to 30 days prior to surgery using a needle and guided imaging of a mammogram., However, non-magnetic tools will need to be used while the Sentimag® probe is being used to detect the magnetic seeds. In the Netherlands, there is another magnetic seed localization technology known as MaMaLoc; however, this is not yet available in Canada.The use of wire localization is the most commonly used option and has documented effectiveness and safety. However, since the wire is external, it may dislodge and can cause discomfort., Additionally, since it needs to be placed ahead of the surgery, there needs to be coordination between wire insertion and surgery., Radioactive seed localization will result in exposure to radioactivity. Using magnetic seeds for localization can avoid these disadvantages.CADTH previously reviewed preoperative seed placement for breast cancer surgery; however, the focus of that report was on the use of radioactive seeds.The purpose of this report is to review the clinical and cost-effectiveness of magnetic seed localization of non-palpable breast lesions, as well as the guidelines for its use.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.020
GPT teacher head0.295
Teacher spread0.275 · 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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

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