Evaluation of the electronic self-report Symptom Screening in Pediatrics Tool (SSPedi)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: We previously developed the paper-based Symptom Screening in Pediatrics Tool (SSPedi) designed for paediatric cancer symptom screening. Objectives were to evaluate and refine the electronic mobile application (app) of SSPedi using the opinions of children with cancer. METHODS: Participants were children 8-18 years of age with cancer. Participants completed electronic SSPedi on their own and then responded to semistructured questions to determine whether they found electronic SSPedi easy or difficult to complete and understand, understood and liked the app features (audio and animation), and understood previously difficult to understand concepts with the introduction of a help menu. After each group of 10 children, responses were reviewed to determine whether modifications were required. RESULTS: 20 children evaluated electronic SSPedi. None found electronic SSPedi difficult to complete or understand. All children understood the app features and each of the 4 more difficult to understand concepts after using the help menu. 19 of 20 children thought the app was a good way to communicate with doctors and nurses. CONCLUSIONS: We finalised an electronic version of SSPedi that is easy to use and understand with features specifically designed to facilitate child self-report. Future work will evaluate the psychometric properties of electronic SSPedi.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it