Agreement between youth and caregiver report of pain and functioning in pediatric sickle cell disease: PedsQL sickle cell disease module
Bibliographic record
Abstract
ABSTRACT: Pain is a primary symptom of sickle cell disease (SCD) and is often severe and chronic. To treat SCD-related pain, proper assessment of SCD pain among youth, including the degree of concordance or agreement between youth and caregiver reports of pain, is essential but has not yet been adequately evaluated. In this study, 525 youth with SCD and their parents were evaluated as part of the Sickle Cell Clinical Research and Intervention Program (SCCRIP) to examine pain rating concordance and predictors of concordance. Youth and parents completed the Pediatric Quality of Life Inventory Sickle Cell Disease module (PedsQL-SCD) to measure pain, pain interference, and pain-related constructs. Disease, clinical, and demographic variables were obtained from the SCCRIP database. Intraclass correlations demonstrated moderate-to-poor consistency between youth and caregiver reports of pain and pain interference (ICCs range from 0.17 to 0.54). Analysis of covariance and regression models found that patient age, frequency of hospitalizations and emergency department (ED) visits, economic hardship, and fetal hemoglobin levels were significantly associated with varying pain-rating agreement levels among parent proxy and child self-report pain. Concordance of pain assessments among youth with SCD and their caregivers using the PedsQL-SCD Module was moderate at best, corroborating prior research. Youth factors predicting discordance among pain-related factors included increased ED visits, older age, and female sex. Collectively, these results bolster the use of integrated pain assessments to reduce parent-child discrepancies, thereby improving the adequacy of SCD-related pain assessment and treatment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".