Patient Reported Outcomes/Metrics Program Trial (PROMPT)—palliative radiation: protocol of a prospective observational feasibility study
Bibliographic record
Abstract
BACKGROUND: Palliative radiotherapy (RT) is commonly used to relieve symptoms and improve quality of life (QOL) for patients with metastatic cancer. Timely recognition of treatment related symptoms and adverse events (AEs), however, remains challenging, as reporting often depends on clinic visits and patient recall. This study explores the feasibility of integrating biometric monitoring, specifically the smart shirt from the Hexoskin Medical System (HMS), to monitor patients with metastatic cancer receiving palliative RT for symptom management. This is complemented by Zamplo, a digital health platform to allow patients and caregivers to report symptoms and well-being. METHODS: The PROMPT trial is a prospective observational feasibility study that aims to recruit 100 patients with metastatic cancer undergoing palliative RT. Adult patients with a confirmed cancer diagnosis who can wear the HMS shirt and use the Zamplo platform are included. To improve accessibility and trial opportunity, patients are fitted with one of 11 sex-adjusted HMS shirt sizes, provided a smartphone, and taught to use Zamplo account. Patients are encouraged to wear the HMS shirt for a minimum of 4 hours daily at baseline, during RT and periodically during follow-up. Patient-reported outcomes (PROs) are being collected using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), the EuroQol 5-Dimension 5-Level, the Person-Centred Coordinated Care Experiences Questionnaire for Cancer Patients (PCC-CA-6), and Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events through Zamplo at baseline and on days 30, 90 and 365, and through ad-hoc patient entries. Zamplo data are complemented with routine clinical symptom screening using ESAS. The primary endpoint is to recruit 100 patients within a 12-month period and to report the frequency of grade 2 or above AEs within 30 days of RT. DISCUSSION: HMS shirt may improve detection and timely management of RT-related AEs and help identify key biometric variables that predict or correspond to AEs and changes in patient well-being. Whereas patients typically accept wearing wrist-worn activity trackers (ATs), this study will determine whether remote monitoring using the HMS shirt is acceptable and feasible in a palliative cancer population. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04983199.
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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 | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".