Evaluation of the Effectiveness of Breastfeeding as a Factor in the Preventionof Breast Cancer
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: As cancer is one of the main causes of fatal illnesses in the world, and breast cancer is responsible for an elevated number of deaths in women, it is important to implement measures to prevent this disease. METHOD: In order to assess the influence of breastfeeding in preventing breast cancer in women, forteen prospective cohort articles are included in this study, and their methodological quality has been assessed through the Newcastle Ottawa quality assessment scale cohort studies. After determining the risk of bias for each case study, those with fewer systematic errors and therefore greater validity, have been selected to demonstrate the relationship they propose exists between breastfeeding and breast cancer. RESULTS: 50% percent of the research included found that breastfeeding does not reduce the risk of breast cancer, while the other 50% argue that it is a protective factor. However, with regards to quality, the case studies that conclude that breastfeeding is not associated with breast cancer have more evidential support. CONCLUSION: It is difficult to establish whether or not breastfeeding prevents breast cancer, given the diversity of conclusions in the literature. Nevertheless, the findings of this study reinforce the importance of developing strategies to improve long-term women's health in the field of prevention.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".