Patterns of Breast Diseases Among Women Attending Breast Diseases Diagnosing Center in Erbil City/Iraq
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Breast diseases in women, whether benign or malignant, are very commonly encountered. The pattern of breast diseases varies within countries. The aim of this study was to identify the patterns of breast diseases and their association with different variables in women attending breast diseases diagnosing center in Erbil city/Iraq.MATERIAL & METHODS: A cross-sectional study conducted at breast diseases diagnosing center in Erbil city from 1st of April till 1st of December /2017. A random sample of 500 women of all age groups and with complete records was recruited. The women were classified according to their final diagnosis into 3 categories: normal, benign and malignant breast disease. The level of significance was <0.05.RESULTS: Benign breast diseases diagnosed among (63%) women while malignant breast diseases comprised (13.2%).The most common presentation was mastalgia and mass (39.2%), mastalgia (37.6%), and mass alone (23.2%). Fibro-adenoma (26.2%) was the commonest benign condition with highest incidence (76.9%) in age group less than 20 years. Malignant breast diseases were increasing with age. Benign breast disease associated (p <0.001) with Nulliparity. Breast cancer reported (p <0.001) more among lactating women.CONCLUSIONS: Benign conditions are the most common diagnosis affecting mainly younger women. Breast cancer though diagnosed less frequently and affecting older age groups, yet its seriousness mandating a thorough assessment of women of different presentations especially that of mass alone or with mastalgia.
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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) | 0.002 | 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".