Effect of bi-weekly supervised training sessions and prepared meals on the body weight and body composition of breast cancer survivors
Bibliographic record
Abstract
Breast cancer is ranked the most common cancer among Canadian women, with statistics showing that 1 in 8 women will be diagnosed with breast cancer in her lifetime. While advanced treatments contribute to higher survival rates, the quality of life to which the breast cancer survivors live is negatively affected. The psychological and physical impact of breast cancer is a challenge during and after treatment, as treatment leave sequalae for breast cancer survivors including fatigue, fear of cancer recurrence, body image issue, depression, all of which lead to a sedentary lifestyle. A sedentary lifestyle is associated with weight gain, which contributes to accumulation of fat tissue. An excess amount of weight that contributes to a high body mass index greater or equal to 25 kg/m2 is associated with negative health outcomes including cardiovascular and metabolic diseases and co-morbidities. Particularly in breast cancer survivors, fat tissue contains properties that produce estrogen, a hormone that promotes the development of breast cancer, hence increasing the risk of recurrence of breast cancer. The inflammatory properties of fat tissue also promote the development of breast cancer. Therefore, while the survival rates of breast cancer increase, emphasis to the management of body weight should be addressed. The combination of physical activity and healthy nutrition is widely known to manage body weight in breast cancer survivors. However, the level physical activity achieved by breast cancer survivors are low, particularly women with obesity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".