Control Group Design, Contamination and Drop-Out in Exercise Oncology Trials: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Important considerations for exercise trials in cancer patients are contamination and differential drop-out among the control group members that might jeopardize the internal validity. This systematic review provides an overview of different control groups design characteristics of exercise-oncology trials and explores the association with contamination and drop-out rates. METHODS: Randomized controlled exercise-oncology trials from two Cochrane reviews were included. Additionally, a computer-aided search using Medline (Pubmed), Embase and CINAHL was conducted after completion date of the Cochrane reviews. Eligible studies were classified according to three control group design characteristics: the exercise instruction given to controls before start of the study (exercise allowed or not); and the intervention the control group was offered during (any (e.g., education sessions or telephone contacts) or none) or after (any (e.g., cross-over or exercise instruction) or none) the intervention period. Contamination (yes or no) and excess drop-out rates (i.e., drop-out rate of the control group minus the drop-out rate exercise group) were described according to the three design characteristics of the control group and according to the combinations of these three characteristics; so we additionally made subgroups based on combinations of type and timing of instructions received. RESULTS: 40 exercise-oncology trials were included based on pre-specified eligibility criteria. The lowest contamination (7.1% of studies) and low drop-out rates (excess drop-out rate -4.7±9.2) were found in control groups offered an intervention after the intervention period. When control groups were offered an intervention both during and after the intervention period, contamination (0%) and excess drop-out rates (-10.0±12.8%) were even lower. CONCLUSIONS: Control groups receiving an intervention during and after the study intervention period have lower contamination and drop-out rates. The present findings can be considered when designing future exercise-oncology trials.
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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 | 0.126 | 0.358 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".