Evaluating the impact of thrombopoietin receptor agonist medications on patient outcomes and quality of life in paediatric immune thrombocytopenia through <scp>semi‐structured</scp> interviews
Bibliographic record
Abstract
Summary Over the last decade, treatment of immune thrombocytopenia (ITP) in children has advanced to include thrombopoietin receptor agonist (TPO‐RA) medications. Concurrently, there has been an increased emphasis on patient‐reported outcomes—especially quality of life—to guide treatment. Assessing the impact of TPO‐RAs on quality of life in paediatric ITP is therefore a priority. In this single‐centre integrative mixed‐methods study, a cohort of children with ITP prescribed a TPO‐RA was identified. These children and/or their caregivers were invited to participate in semi‐structured interviews focussed on quality‐of‐life measures. Independently, a retrospective chart review collected ITP‐related data (platelet count, bleeding events) and TPO‐RA data (dosing, side effects). Among the 23 eligible patients, 20 were represented in interviews. On chart review, 11/20 patients responded to TPO‐RA by meeting platelet count criteria of ≥50 × 109/L for six or more weeks in the absence of rescue therapy. In interviews with these children and/or their parents, 19/20 expressed the TPO‐RA had ‘worked’, with 11/20 reporting benefit to mood and 11/20 reporting increased participation in activities/sports. Concerns were raised in interviews about TPO‐RA medication cost (17/20), medication administration (10/20) and potential side effects (10/20). In conclusion, this study suggests that TPO‐RA use in children with ITP improves quality of life.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".