Describing symptoms using the Symptom Screening in Pediatrics Tool in hospitalized children with cancer and hematopoietic stem cell transplant recipients
Bibliographic record
Abstract
Objectives were to describe any bothersome symptom and severely bothersome symptoms in inpatient children with cancer and hematopoietic stem cell transplant (HSCT) recipients. We included children 8-18 years of age with cancer or HSCT recipients who were receiving active treatment for cancer, admitted to hospital, and expected to be in hospital 3 days later. We administered the self-report Symptom Screening in Pediatrics Tool (SSPedi). We described those who identified any degree of symptom bother (at least "a little") and those who rated the degree of bother as severe ("a lot" or "extremely"). Factors associated with severe symptoms and total SSPedi scores were examined using multiple logistic and linear regression. Among the 302 patients, 298 (98.7%) reported having any bothersome symptom and 181 (59.9%) had at least one severely bothersome symptom. In multiple regression, older children were significantly more likely to have at least one severely bothersome symptom (15-18 and 11-14 years vs. 8-10 years; P = 0.008) and to have higher total SSPedi scores (P = 0.0003). Those with relapsed disease were more likely to have at least one severely bothersome symptom (odds ratio 2.1, 95% confidence interval 1.1-4.3; P = 0.037) and HSCT recipients were more likely to have higher symptom scores (β = 3.48, standard error = 1.6; P = 0.030). Almost all children receiving cancer therapies experience bothersome symptoms and 60% have at least one severely bothersome symptom. Older children experienced more severely bothersome symptoms and higher symptom scores. Future studies should follow children longitudinally to better understand the symptom trajectory and should institute interventions to manage 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".