Non-invasive wearable devices in paediatric cancer care: Advancing personalized medicine, addressing challenges and shaping the future
Bibliographic record
Abstract
Wearable devices (WDs) are capable of collecting large volumes of objective and clinically relevant patient data that is not yet routinely captured. This ability to collect continuous, real-time data offers a unique opportunity to gather health information in new and insightful ways. In paediatric oncology, advancement in treatment have led to significant improvements in survival rates. However, aggressive therapies often result in a range of distressing side effects, which can severely impact quality of life, and even can become life-threatening themselves. Supportive care plays a crucial role in mitigating these symptoms, aiming to prevent and manage side effects. Patient-reported outcomes should be used to guide initiation and choice of supportive care treatment whenever possible. In this context, continuous monitoring of vital signs, physical activity and other health parameters using WDs could add individual, patient specific information regarding a patient's current condition. In this article we discuss the requirements of non-invasive WDs for their use in paediatric oncology, give an overview on possible areas of application in children with cancer and discusses challenges that must be addressed. Also we identify key research gaps and speculate on future perspectives.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".