Increased KI67 Immunostaining is Associated with Breast Cancer Aggressiveness
Bibliographic record
Abstract
Background: Breast cancer remains one of the most prevalent tumors among females worldwide, and current prognostic methods are limited in their ability to accurately predict tumor aggressiveness and long-term outcomes. KI67 is a nuclear protein marking active cell proliferation and is associated with tumor differentiation, growth, and breast cancer subtypes. Objective: This study aims to assess KI67 in benign and malignant breast tissues and explore its potential association with clinical outcomes. Methodology: Immunohistochemistry was used to assess KI67 staining in benign (n=57) and malignant (n=430) breast tissue samples. KI67 expression was then correlated with clinic opathological data, such as grade and stage, and breast cancer molecular subtypes. Results: Increased KI67 was significantly found in breast carcinoma compared to benign tissues (P <0.0001). A positive association was observed between KI67 immunostaining and tumor grade (P <0.0001), tumor size (P0.007), and molecular subtype (P 0.002).No significant relationship was recognized between KI67 immunostaining and lymph node involvement. Conclusion: KI67 might be involved in the aggressiveness of malignancy. Further research is needed to determine the significance of KI67 inbreast carcinoma and its potential as a biomarker for breast cancer progression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".