Kejadian Mortalitas Wanita dengan Kanker Payudara Berdasarkan Indeks Massa Tubuh (BMI): Tinjauan Naratif
Bibliographic record
Abstract
Breast cancer is the most common cancer found in women of all types of cancer in the world. The relationship between body mass index and the death rate from breast cancer in women has drawn attention recently. This study sought to ascertain the relevance of the variation in death rates between women with breast cancer who had a normal body mass index (BMI) and those who had a BMI of ≥25. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) references were used to guide the systematic review process in this investigation. The Newcastle-Ottawa Scale for cohort design studies and the Robins-I test for single-arm experimental design studies were used to assess the quality of the articles. The data source consisted of 142 Pubmed publications published between 2016 and 2023. The analysis's findings revealed differences between the five publications that discussed the connection between obesity and breast cancer. The development of breast cancer is linked to an increase in leptin and estrogen, which is consistent with an increase in fat. It is concluded that individuals with a body mass index (BMI) of ≥ 25 had a poorer chance of surviving breast cancer than patients with a normal BMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".