Pain Squad+ smartphone app to support real-time pain treatment for adolescents with cancer: protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Pain negatively affects the health-related quality of life (HRQL) of adolescents with cancer. The Pain Squad+ smartphone-based application (app), has been developed to provide adolescents with real-time pain self-management support. The app uses a validated pain assessment and personalised pain treatment advice with centralised decision support via a registered nurse to enable real-time pain treatment in all settings. The algorithm informing pain treatment advice is evidence-based and expert-vetted. This trial will longitudinally evaluate the impact of Pain Squad+, with or without the addition of nurse support, on adolescent health and cost outcomes. METHODS AND ANALYSIS: This will be a pragmatic, multicentre, waitlist controlled, 3-arm parallel-group superiority randomised trial with 1:1:1 allocation enrolling 74 adolescents with cancer per arm from nine cancer centres. Participants will be 12 to 18 years, English-speaking and with ≥3/10 pain. Exclusion criteria are significant comorbidities, end-of-life status or enrolment in a concurrent pain study. The primary aim is to determine the effect of Pain Squad+, with and without nurse support, on pain intensity in adolescents with cancer, when compared with a waitlist control group. The secondary aims are to determine the immediate and sustained effect over time of using Pain Squad+, with and without nurse support, as per prospective outcome measurements of pain interference, HRQL, pain self-efficacy and cost. Linear mixed models with baseline scores as a covariate will be used. Qualitative interviews with adolescents from all trial arms will be conducted and analysed. ETHICS AND DISSEMINATION: This trial is approved by the Hospital for Sick Children Research Ethics Board. Results will provide data to guide adolescents with cancer and healthcare teams in treating pain. Dissemination will occur through partnerships with stakeholder groups, scientific meetings, publications, mass media releases and consumer detailing. TRIAL REGISTRATION NUMBER: ).
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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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.113 | 0.018 |
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".