[Analysis on clinical and pathological characteristics of 66 patients with stage IV breast cancer].
Bibliographic record
Abstract
OBJECTIVES: To explore the clinical and pathological characteristics of stage IV breast cancer and to analyze their relationship with the morbidity and prognosis. METHODS: The records of 66 patients presenting from January 2008 to December 2014 with stage IV breast cancer were reviewed. All of the patients were women and the median age was 57.5 (31 to 80) years, accounted for 3.01% (66/2 189) among the breast cancer patients treated in the same period. Statistical methods were used to analyze the correlation between clinical and pathological characteristics such as T-stage, N-stage, immuno-histo-chemistry and the morbidity and prognosis of stage IV breast cancer. The influence of patients characteristics to metastasis were compared by χ(2) test. Kaplan-Meier curves were reported for overall survival (OS), and the Log-rank test was used to compare the difference in groups. Cox proportional models were fitted for multivariate analysis. RESULTS: The median survival time of stage IV breast cancer was 56.0 months and the 5-year survival rate was 40%. To metastasis, the effects of age, subtypes, histological grade, hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2)had no significant statistics differences. It was concluded that the expression of HER2 (P=0.003) and HR (P=0.001) as well as single metastasis (P=0.029) were the influencing factors of the survival by multivariate Cox regression analysis. Primary tumor R0 surgery group and no surgery group had no significant statistics differences of overall survival and the 5-year survival rate (P=0.102). CONCLUSIONS: Clinical and pathological characteristics have no effect on metastasis. The expression of HER2 and HR as well as single metastasis play important roles in survival.
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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 | 0.001 |
| 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) | 0.003 | 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".