Reporting Rigor of Cancer Rehabilitation Interventions
Bibliographic record
Abstract
ABSTRACT: Clear reporting of cancer rehabilitation interventions is critical for interpreting and translating research into clinical practice. This study sought to examine the completeness of intervention reporting of cancer rehabilitation interventions addressing disability and to identify which elements are most frequently missing. This was a secondary analysis of randomized controlled trials included in two systematic reviews examining effectiveness of cancer rehabilitation interventions that address cancer-related disability, including functional outcomes. Eligible trials were reviewed for intervention reporting rigor using the Criteria for Reporting the Development and Evaluation of Complex Interventions in Healthcare 2 checklist. Intervention descriptions for cancer rehabilitation interventions were generally incomplete. Approximately 85% ( n = 157) of trials described ≤50% of Criteria for Reporting the Development and Evaluation of Complex Interventions in Healthcare 2 checklist items. Commonly underreported items included description of the intervention's underlying theoretical basis, fidelity, description of process evaluation or external conditions influencing intervention delivery, and costs or required resources for intervention delivery. The findings reveal that cancer rehabilitation intervention descriptions lacked necessary detail in this body of literature. Poor descriptions limit the translation of research to clinical practice. To ensure higher-quality study design and reporting, future intervention research should incorporate an intervention reporting checklist to ensure more complete descriptions for 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 | 0.775 | 0.904 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".