Treatment Does Not Correlate with Quality of Life in Children with Immune Thrombocytopenic Purpura: Results From the KIT International Cross-Cultural Validation Study.
Bibliographic record
Abstract
Abstract Abstract 2501 Poster Board II-478 Introduction: There is considerable controversy surrounding whether or not children with Immune Thrombocytopenic Purpura (ITP) should be treated if they present without bleeding. One of the potential benefits of treatment would be to improve the child's health-related quality of life (HRQoL). Patients and Methods: Variables including age, sex, type of ITP(acute versus chronic), treatment (observation, IVIG, Anti-D, and prednisone), platelet count and country of origin were analysed by multiple regression to determine their relationship to HRQoL as measured by the Kid's ITP Tools (KIT) child self-report version. Results: 77 children from Uruguay (n=15), France (n=25), Germany (n=13) and the UK (n=24) self-completed the KIT. Mean platelet counts were: 6 for acute ITP patients (mean age 8.6 yrs) and 30 for chronic patients (mean age of 10.8 years). KIT scores by type of ITP and country are shown in the Figure. Multiple regression found that only the type of ITP (p=0.04) and the country of origin (p=0.029) were significantly associated with the KIT scores. Age, sex, platelet count and treatment were not correlated with KIT scores (p>0.16). Conclusion: The method of treatment did not have a significant impact on child-reported HRQoL. However, differences in HRQoL scores were found between acute and chronic groups and by country of origin. Disclosures: Klaassen: Cangene : Research Funding. Blanchette:Cangene: Research Funding. Young:Cangene: Research Funding.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".