Framework PEACE: An organizational model for examining physical exercise across the cancer experience
Bibliographic record
Abstract
The primary purpose of this article is to provide a framework for organizing research on physical exercise and cancer control. A secondary purpose is to use this framework to provide an overview of the extant literature and to offer directions forr future research. The proposed framework, entitled Physical Exercise Across the Cancer Experience (PEACE), divides the cancer experience into 6 time periods: 2 prediagnosis (i.e., prescreening and screening/diagnosis) and 4 postdiagnosis (i.e., pretreatment, treatment, posttreatment, and resumption). Based on these time periods, 8 general cancer control outcomes are highlighted. Two cancer control outcomes occur prediagnosis (i.e., prevention and detection), and 6 occur postdiagnosis (i.e., buffering, coping, rehabilitation, health promotion, palliation, and survival). An overview of the physical exercise literature indicates that only I time period (i.e., prescreening) and cancer control outcome (i.e., prevention) has received significant research attention. Some time periods (i.e., treatment and resumption) and cancer control outcomes (i.e., coping and health promotion) have received modest research attention, whereas other time periods (i.e., screening/diagnosis, pretreatment, and posttreatment) and cancer control outcomes (i.e., detection, buffering, rehabilitation, palliation, and survival) have received only minimal attention. It is hoped that Framework PEACE will stimulate a more comprehensive and in-depth inquiry into the role of physical exercise in cancer control.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".