Accelerating precision exercise medicine in cancer patients using pooled individual patient data: POLARIS experience
Bibliographic record
Abstract
Numerous exercise oncology trials have been completed, greatly informing exercise recommendations for patients with cancer. Exercise medicine can be administered in various types, doses, and schedules at various time points. Advancing precision exercise medicine requires understanding of how the effects of different exercise interventions vary by characteristics of individual patients. The Predicting OptimaL cAncer RehabIlitation and Supportive care (POLARIS) study provides an international infrastructure and shared database to perform pooled analyses of individual patient data (IPD) from multiple randomized controlled trials. This commentary aims to highlight the value of pooled IPD analyses, summarize key findings from published pooled IPD analyses on the effects of physical exercise on various outcomes, and provide guidance to advance precision exercise medicine for patients with cancer. POLARIS currently includes IPD from 52 exercise trials. Findings to date indicate that exercise interventions in patients with cancer have beneficial effects on physical fitness, fatigue, health-related quality of life, self-reported cognition (posttreatment), sleep disturbances, and symptoms of anxiety and depression. Additionally, it was determined that the exercise effects varied by characteristics of the patients, including the initial value of the outcome, age, marital status, and education level, and by characteristics of the intervention, including exercise supervision and specificity. Future research opportunities to advance precision exercise medicine for patients with cancer include pooling of trial data from understudied populations, data on clinical outcomes, and biomarkers, as well as applying machine learning models for identifying combinations of covariables that modify intervention effects and predictions of individual treatment effects.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".