Cryoablation and cryolocalization in the management of breast disease
Bibliographic record
Abstract
Cryotechnology is currently used for both treatment and diagnosis of breast disease. Due to the natural analgesic effect of cold, cryoablation is potentially more patient-friendly than other technologies which raise tissue temperature. Freezing produces a predictable volume of necrosis and is easily observed and controlled during treatment. Recent studies have demonstrated that, as a primary therapy for breast fibroadenoma, cryoablation is safe and effective with durable results that can be reproduced in community practices. Certain barriers do exist before cryoablation, or any other in situ ablation, can become a standard therapy for the treatment of localized breast malignancy. Investigations are underway to refine patient selection criteria and develop valid confirmatory assays so that clinical trials can begin. Cryolocalization, which creates a well-delineated, palpable mass of frozen tissue encompassing a tumor, is a relatively new application of cold in medicine. This strategy promises to reduce positive margin rates during lumpectomy of non- or barely-palpable tumors. Finally, cryotechnology now also aids in the collection of tissue for histological analysis.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".