Longitudinal evaluation of Supportive care Prioritization, Assessment and Recommendations for Kids (SPARK), a symptom screening and management application
Bibliographic record
Abstract
BACKGROUND: Supportive care Prioritization, Assessment and Recommendations for Kids (SPARK) is a web application focused on improving symptom control. It enables pediatric cancer and hematopoietic stem cell transplant (HSCT) patients to self-report and track symptoms, and allows healthcare professionals to access guidelines for symptom management. Objective was to determine the feasibility of longitudinal collection of symptom data. METHODS: In this longitudinal, single-armed feasibility study, respondents were children 8-18 years of age with cancer or pediatric HSCT recipients. Participants completed symptom reporting daily for 5 days. Cognitive interviews were conducted on day 5. Quantitative evaluation included SPARK ease of use and understandability of SPARK reports. Qualitative feedback on facilitators and barriers to daily symptom screening was solicited. Feasibility was defined as ≥75% of participants completing symptom screening on at least 60% of on-study days during the five-day study. RESULTS: Among the 30 children enrolled, the median number of days SSPedi was completed at least once was 5 (range 3 to 5). Overall, 28/29 (96.6%) thought completing symptom screening using SPARK was easy or very easy. All participants understood SPARK symptom reports. Severe symptoms was the most common barrier to daily reporting while an alarm reminder system was the most commonly identified facilitator. CONCLUSIONS: Daily completion of symptom screening using SPARK over 5 days was feasible in children aged 8 to 18 years with cancer and pediatric HSCT recipients. SPARK is now appropriate for use in randomized trials to evaluate the effect of symptom screening and symptom feedback.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".