The Role of Obesity in Cancer Survival and Recurrence
Bibliographic record
Abstract
Obesity and components of energy imbalance, that is, excessive energy intake and suboptimal levels of physical activity, are established risk factors for cancer incidence. Accumulating evidence suggests that these factors also may be important after the diagnosis of cancer and influence the course of disease, as well as overall health, well-being, and survival. Lifestyle and medical interventions that effectively modify these factors could potentially be harnessed as a means of cancer control. However, for such interventions to be maximally effective and sustainable, broad sweeping scientific discoveries ranging from molecular and cellular advances, to developments in delivering interventions on both individual and societal levels are needed. This review summarizes key discussion topics that were addressed in a recent Institute of Medicine Workshop entitled, "The Role of Obesity in Cancer Survival and Recurrence"; discussions included (i) mechanisms associated with obesity and energy balance that influence cancer progression; (ii) complexities of studying and interpreting energy balance in relation to cancer recurrence and survival; (iii) associations between obesity and cancer risk, recurrence, and mortality; (iv) interventions that promote weight loss, increased physical activity, and negative energy balance as a means of cancer control; and (v) future directions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".