Impact of Evaluation of Tumour Grade by Core Needle Biopsy on Clinical Risk Assessment and Patient Selection for Adjuvant Systemic Treatment in Breast Cancer
Bibliographic record
Abstract
Introduction: Breast cancer is one of the most common cancers affecting women worldwide. The prognosis and treatment of breast cancer depend largely on various prognostic factors, including tumour grade, hormone receptor status, and HER2/neu overexpression. Objectives: The main objective of the study is to find the impact of evaluation of tumour grade by core needle biopsy on clinical risk assessment and patient selection for adjuvant systemic treatment in breast cancer Material and Methods: This retrospective cohort study was conducted in Services Hospital, Lahore during January 2020 to January 2021. The study participants were women with breast cancer who underwent core needle biopsy for tumour grade evaluation at a single institution during the study period. Results: Of the 70 patients included in the study, the mean age was 58 years (range, 32-85 years), and the majority were postmenopausal (60%). Most patients had invasive ductal carcinoma (IDC) (85%), and the remainder had invasive lobular carcinoma (ILC) (15%). Most patients had stage II (45%) or stage III (35%) breast cancer at the time of diagnosis. All patients underwent core needle biopsy for tumour grade evaluation. Conclusion: In conclusion, the study supports the use of core needle biopsy as a reliable method for evaluating tumour grade in breast cancer patients. Further research is needed to evaluate the long-term outcomes of patients who are treated based on tumour grade assessed by core needle biopsy.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".