Magnetic seed localization for soft tissue lesions in breast patients: Clinical effectiveness, cost-effectiveness, and guidelines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".