Using a Heuristic Tool to Improve Symptom Self-Management in Adolescents and Young Adults With Cancer: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Background: Adolescents and young adults (AYAs) with cancer experience multiple distressing symptoms during treatment, yet few developmentally relevant resources have been developed to help them self-manage their symptoms. Empowering patients to have a more active role in self-management during cancer treatment may lessen their symptom severity and distress. Objective: The aim of this study is to test an intervention designed to improve symptom self-management, the Computerized Symptom Capture Tool (C-SCAT), by helping AYAs understand their unique symptom experience and discuss it with their health care providers. Methods: We are conducting a multisite, 2-group randomized controlled trial to evaluate the effects of the C-SCAT in improving symptom self-management versus usual care in 126 AYAs who are within the first 3 months of a cancer diagnosis and who are receiving chemotherapy. Participants are randomly assigned to either the C-SCAT intervention group or the usual care group. The primary aim is to determine the effects of the C-SCAT versus usual care on the primary outcomes of self-efficacy for symptom management and symptom self-management behaviors. The secondary aim is to examine the effects of the C-SCAT versus usual care on distal outcomes, including symptoms and quality of life. Participants complete measures of self-efficacy for symptom management, symptom self-management behaviors, symptom severity and distress, quality of life social function, and quality of life satisfaction of social function at baseline (time 0), immediately postintervention (time 1), and at follow-up 1 month later (time 2). Results: Recruitment started on January 4, 2024, at the first site, and all sites were open by May 1, 2024. Thus far, accrual has been set at 73% of the quarterly benchmark goal. Linear mixed effects models will be used to test for group differences across time for the primary and secondary aims. Conclusions: This randomized controlled trial is evaluating an innovative, point-of-care intervention designed to be used at a clinic visit with the provider to improve symptom self-management for AYAs with cancer.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Randomized trial | medium |
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 | 0.032 | 0.034 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.065 | 0.010 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".