Initial development of Supportive care Assessment, Prioritization and Recommendations for Kids (SPARK), a symptom screening and management application
Bibliographic record
Abstract
BACKGROUND: We developed Supportive care Prioritization, Assessment and Recommendations for Kids (SPARK), a web-based application designed to facilitate symptom screening by children receiving cancer treatments and access to supportive care clinical practice guidelines primarily by healthcare providers. The objective was to describe the initial development and evaluation of SPARK from the perspective of children. IMPLEMENTATION: Development and evaluation occurred in three phases: (1) low fidelity focused on functionality, (2) design focused on "look and feel" and (3) high fidelity confirmed functionality and design. Cognitive interviews were conducted with children receiving cancer treatments 8-18 years of age. Evaluation occurred after every five interviews and changes were guided by a Review Panel. Quantitative evaluation included SPARK ease of use and understandability of SPARK reports. RESULTS: The number of children included by phase were: low fidelity (n = 30), design (n = 30) and high fidelity (n = 30). Across phases, the median age was 13.2 (range 8.5 to 18.4) years. During low-fidelity and design phases, iterative refinements to SPARK improved website navigation, usability and likability from the perspective of children and established symptom report design. Among the last 10 children enrolled to high-fidelity testing, all (100%) understood how to complete symptom screening, access reports and interpret reports. Among these 10 respondents, all (100%) found SPARK easy to use and 9 (90%) found SPARK reports were easy to understand. CONCLUSIONS: SPARK is a web-based application which is usable and understandable, and it is now appropriate to use for research. Future efforts will focus on clinical implementation of SPARK.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".