Validation of the Symptom Screening in Pediatrics Tool in Children Receiving Cancer Treatments
Bibliographic record
Abstract
Background: The objective was to evaluate the reliability and validity of the self-report Symptom Screening in Pediatrics Tool (SSPedi) from the perspective of children with cancer and pediatric hematopoietic stem cell transplant (HSCT) recipients. Methods: In this multicenter study, respondents were children age eight to 18 years who had cancer or had received HSCT, and their parents. Two different child respondent populations were targeted. More symptomatic respondents were receiving active treatment for cancer, admitted to the hospital, and expected to be in the hospital three days later. Less symptomatic respondents were in maintenance therapy for acute lymphoblastic leukemia or had completed cancer therapy. Children completed SSPedi and then responded to validated self-report measures of mucositis, nausea, pain, and global quality of life. Children in the more symptomatic group repeated SSPedi and a global symptom change scale three days later. Parent proxy-report was optional. Reliability was evaluated using intraclass correlations while convergent validity was evaluated using Spearman correlations. Results: Of 502 children enrolled, 302 were in the more symptomatic group and 200 were in the less symptomatic group. Intraclass correlation coefficients were 0.88 (95% confidence interval [CI] = 0.82 to 0.92) for test-retest reliability and 0.76 (95% CI = 0.71 to 0.80) for inter-rater reliability. The mean difference in SSPedi scores between more and less symptomatic groups was 7.8 (95% CI = 6.4 to 9.2). SSPedi was responsive to change in global symptoms. All hypothesized relationships among measures were observed. Conclusions: SSPedi is a self-report symptom bother tool for children with cancer and HSCT recipients that is reliable, valid, and responsive to change. SSPedi can be used for clinical and research purposes. Future work should focus on integration into care delivery.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".