Breast Cancer Status in Iran: Statistical Analysis of 3010 Cases between 1998 and 2014
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the 5th leading cause of cancer death in Iranian women. This study analyzed 3010 women with breast cancer that had been referred to a cancer research center in Tehran between 1998 and 2014. METHODS: In this retrospective study, we analyzed 3010 breast cancer cases with 32 clinical and paraclinical attributes. We checked the data quality rigorously and removed any invalid values or records. The method was data mining (problem definition, data preparation, data exploration, modeling, evaluation, and deployment). However, only the descriptive analyses' results of the variables are presented in this article. To our knowledge, this is the most comprehensive study on breast cancer status in Iran. RESULTS: A typical Iranian breast cancer patient has been a 40-50-year-old married woman with two children, who has a high school diploma and no history of abortion, smoking, or diabetes. Most patients were estrogen and progesterone receptor positive, human epidermal growth factor (HER) negative, and P53 negative. Most cases were detected in stage 2 with intermediate grade. CONCLUSION: This study revealed original findings which can be used in national policymaking to find the best early detection method and improve the care quality and breast cancer prevention in Iran.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".