Designing, analyzing, and interpreting observational studies of physical activity and cancer outcomes from a clinical oncology perspective
Bibliographic record
Abstract
Observational studies may play an important role in evaluating physical activity (PA) as a cancer treatment; however, few studies have been designed, analyzed, or interpreted from a clinical oncology perspective. The purpose of the present paper is to apply the Exercise as Cancer Treatment (EXACT) Framework to assess current observational studies of PA and cancer outcomes from a clinical oncology perspective and provide recommendations to improve their clinical utility. Recent systematic reviews and meta-analyses of over 130 observational studies have concluded that higher prediagnosis and postdiagnosis PA are associated with lower risks of cancer-specific and all-cause mortality. Most of these studies, however, have: (a) included cancer patients receiving heterogeneous treatment protocols, (b) provided minimal details about those cancer treatments, (c) assessed PA prediagnosis and/or postdiagnosis without reference to those cancer treatments, (d) reported mainly mortality outcomes, and (e) examined subgroups based on demographic and disease variables but not cancer treatments. As a result, current observational studies on PA and cancer outcomes have played a modest role in informing clinical exercise trials and clinical oncology practice. To improve their clinical utility, we recommend that future observational studies of PA and cancer outcomes: (a) recruit cancer patients receiving the same or similar first-line treatment protocols, (b) collect detailed data on all planned and unplanned cancer treatments beyond whether or not cancer treatments were received, (c) assess PA in relation to cancer treatments (i.e., before, during, between, after) rather than in relation to the cancer diagnosis (i.e., various time periods before and after diagnosis), (d) collect data on cancer-specific outcomes (e.g., disease response, progression, recurrence) in addition to mortality, (e) conduct subgroup analyses based on cancer treatments received in addition to demographic and disease variables, and (f) interpret mechanisms for any associations between PA and cancer-specific outcomes based on the clinical oncology scenario that is recapitulated rather than referencing generic mechanisms or discordant preclinical models. In conclusion, observational studies are well-suited to contribute important knowledge regarding the role of PA as a cancer treatment; however, modifications to study design and analysis are necessary if they are to inform clinical research and practice.
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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 | 0.443 | 0.720 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".