Kejadian Mortalitas Wanita dengan Kanker Payudara Berdasarkan \nIndek Massa Tubuh (BMI): Tinjauan Naratif \nIncident Of Mortality In Women With Breast Cancer Based On Body Mass \nIndex (BMI): A Narrative Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Breast cancer is the most common cancer found in women of all types of cancer in the \nworld. The relationship between body mass index and the death rate from breast cancer \nin women has drawn attention recently. This study sought to ascertain the relevance of \nthe variation in death rates between women with breast cancer who had a normal body \nmass index (BMI) and those who had a BMI of ≥25. Preferred Reporting Items for \nSystematic Reviews and Meta-Analyses (PRISMA) references were used to guide the \nnarrative review process in this investigation. The Newcastle-Ottawa Scale for cohort \ndesign studies and the Robins-I test for single-arm experimental design studies were used \nto assess the quality of the articles. The data source consisted of 142 Pubmed \npublications published between 2016 and 2023. The analysis's findings revealed \ndifferences between the five publications that discussed the connection between obesity \nand breast cancer. The development of breast cancer is linked to an increase in leptin \nand estrogen, which is consistent with an increase in fat. It is concluded that individuals \nwith a body mass index (BMI) of ≥ 25 had a poorer chance of surviving breast cancer \nthan 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it