A remotely delivered exercise-based rehabilitation program for patients with persistent chemotherapy-induced peripheral neuropathy (EX-CIPN): Protocol for a phase I feasibility trial
Bibliographic record
Abstract
BACKGROUND: Chemotherapy-induced peripheral neurotoxicity (CIPN) is a prevalent adverse effect of chemotherapy agents that is estimated to be present in 2/3 of patients who receive neurotoxic chemotherapy. In 30-40% of these patients, CIPN signs and symptoms can persist for months or years post-treatment. Recent studies have supported exercise as a feasible and possibly effective intervention for CIPN; however, more rigorous studies are needed to confirm feasibility, estimate efficacy, and clarify risk. In response, we developed an innovative virtual exercise-based rehabilitation program (EX-CIPN) for cancer survivors with persistent CIPN. METHODS: This study is a phase I study conducted at the Princess Margaret Cancer Centre in cancer survivors with persistent CIPN, with a focus on feasibility, acceptability, and safety. A total of 40 patients aged 18 or older, with persistent CIPN at least 6 months after chemotherapy completion will be recruited and receive the EX-CIPN program. The EX-CIPN program is a 10-week virtual home-based intervention that includes an individualized exercise program supported with a mobile application (Physitrack), wearable technology (FitBit), and weekly virtual check-ins with an oncology exercise specialist. The primary outcome of feasibility will be assessed by examining accrual, retention, and adherence rates. Acceptability will be assessed through qualitative interviews. Safety events will be monitored and reported based on CTCAE v5. Secondary outcomes will be collected using questionnaires and physiological assessments at baseline (T1), after the intervention (T2), and 3-months after intervention (T3). CONCLUSION: This phase I study will determine intervention feasibility, acceptability, and safety and will inform the planning for a future Phase II RCT with the EX-CIPN intervention.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".