Patterns and influences in health‐related quality of life in children with immune thrombocytopenia: A study from the Dallas ITP Cohort
Bibliographic record
Abstract
BACKGROUND: Relationships between clinical/demographic factors and health-related quality of life (HRQoL) in childhood immune thrombocytopenia (ITP) remain poorly understood. Recent studies reveal conflicting information about factors that contribute to HRQoL. METHODS: This was a prospective, single-institution, cohort study of newly diagnosed children with ITP. Serial evaluations of HRQoL were performed using the Kid's ITP Tools (KIT), scored from 0 (worst) to 100 (best), at enrollment and 1 week, 6 months, and 12 months following diagnosis. All visits included bleeding severity grading. Relationships between HRQoL and platelet count, treatment, bleeding severity, and course of disease were examined. RESULTS: A total of 99 children with newly diagnosed ITP were evaluable for analysis. KIT scores were low at diagnosis for parents (median 26, range 15-43) and children (median 65, range 55-81) and were not influenced by age or platelet count. At diagnosis, children who received treatment had lower platelet counts (P = 0.005), more severe hemorrhage (P < 0.0125), and lower HRQoL by parent, child, and proxy reporting (P < 0.05). Oral bleeding negatively impacted proxy-reported disease burden at diagnosis (P = 0.01). Persistence of disease and lower platelet counts at 6 and 12 month visits were the only factors noted to consistently impact quality of life beyond diagnosis for both parents and children. CONCLUSIONS: HRQoL is low at diagnosis but significantly improves over time. Patients with ongoing disease and lower platelet counts continue to have significant disease burden.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".