Bibliographic record
Abstract
Both immunohistochemical and imaging methods and non-invasive biomarkers (microribonucleic acids, etc.) are used for the early diagnosis of breast cancer. A total of 128 women with breast cancer with an average age of 59.4811.99 years (between 30 and 84 years), operated on between December 1, 2017 and November 30, 2020, were examined in Dr. Marko Markov Varna Oncology. The expression of estrogen and progesterone receptors in breast biopsies and operative materials was analyzed by indirect immunoperoxidase method with EnVision FLEX MiniKit, that of human epidermal growth factor receptor-2-with HercepT-est, and that of the proliferation index Ki-67-with the Leica Aperio Scan Scope AT2. Triple negative breast cancer was diagnosed in 15 patients (in 11.72% of cases). The average age of the patients was 56.2712.83 years (between 32 and 78 years). Eight patients were in the age groups between 41 and 60 years, and six patients were in the age groups between 61 and 80 years. Patients with ductal invasive carcinoma predominated (seven or 46.67%), followed by those with ductal carcinoma (four or 26.67%), carcinoma not otherwise specified (three or 20.00%), and non-specific invasive carcinoma (one patient or 6.67% of cases). Seven patients each had degrees of differentiation of G2 and G3. For G2, it concerned three patients aged between 42 and 50 years, two patients-aged between 71 and 80 years and one patient each aged between 51 and 60 years and between 61 and 70 years, and for G3-two patients aged between 51 and 60 years and between 61 and 70 years, and one patient from each of the following the age groups: between 31 and 40 years, between 41 and 50 years, and between 71 and 80 years. The obtained results were of benefit in the choice of treatment for these patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.729 | 0.629 |
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".