Symptom Screening in Pediatrics Tool in children and adolescents with high-risk malignancies: a pilot study
Bibliographic record
Abstract
OBJECTIVE: Childhood and adolescent cancer can result in high burden of distressing symptoms, particularly in high-risk malignancies. The Symptom Screening in Pediatrics Tool (SSPedi) is a reliable and valid approach to measure bothersome symptoms in paediatric patients receiving cancer treatments. Objective was to describe the feasibility of using SSPedi administration among paediatric patients with high-risk malignancies. METHODS: We conducted a single-centre, cross-sectional study of patients aged 8-18 years with high-risk malignancies in a French paediatric oncology unit. Patients self-reported the degree of bothersome symptoms using SSPedi and difficulty with SSPedi completion. The total SSPedi Score ranging from 0 to 60 (where 60 is worst) and most common moderately bothersome symptoms (scored ≥2 on 0-4 Likert Scale) were described. Feasibility was defined as more than 75% of patients agreeing to participate and more than 90% completion of SSPedi questionnaire. RESULTS: Out of 16 patients approached, 1 declined participation. Median age was 13 years (IQR 8-19). All were able to self-report SSPedi without difficulty. Patients experienced a median number of 6 (range 0-15) bothersome symptoms (score >0). The mean total SSPedi Score was 12 (SD=9.4). Most common moderately bothersome symptoms were pain (8/15), changes in hunger (8/15) and feeling tired (7/15). CONCLUSION: Patient-reported symptom assessment among children and adolescents with high-risk malignancies is feasible using SSPedi. These patients experience a high burden of bothersome symptoms.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".