Associations of low‐carbohydrate diets with breast cancer survival
Bibliographic record
Abstract
Abstract Background The objective of this study was to evaluate the role of low‐carbohydrate diets after breast cancer diagnosis in relation to breast cancer–specific and all‐cause mortality. Methods For 9621 women with stage I–III breast cancer from two ongoing cohort studies, the Nurses’ Health Study and Nurses’ Health Study II, overall low‐carbohydrate, animal‐rich low‐carbohydrate, and plant‐rich low‐carbohydrate diet scores were calculated by using food frequency questionnaires collected after breast cancer diagnosis. Results Participants were followed up for a median 12.4 years after breast cancer diagnosis. We documented 1269 deaths due to breast cancer and 3850 all‐cause deaths. With the use of Cox proportional hazards regression and after controlling for potential confounding variables, we observed a significantly lower risk of overall mortality among women with breast cancer who had greater adherence to overall low‐carbohydrate diets (hazard ratio for quintile 5 vs. quintile 1 [HRQ5vsQ1], 0.82; 95% CI, 0.74–0.91; ptrend = .0001) and plant‐rich low‐carbohydrate diets (HRQ5vsQ1, 0.73; 95% CI, 0.66–0.82; ptrend < .0001) after breast cancer diagnosis but not animal‐rich low‐carbohydrate diets (HRQ5vsQ1, 0.93; 95% CI, 0.84–1.04; ptrend = .23). However, greater adherence to overall, animal‐rich, or plant‐rich low‐carbohydrate diets was not significantly associated with a lower risk of breast cancer–specific mortality. Conclusions This study showed that greater adherence to low‐carbohydrate diets, especially plant‐rich low‐carbohydrate diets, was associated with better overall survival but not breast cancer–specific survival among women with stage I–III breast cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".