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Quality of life in immune thrombocytopenia following treatment

2013· article· en· W2076281034 on OpenAlexaff
John D. Grainger, Nancy L. Young, Victor S. Blanchette, Robert J. Klaassen, Vivien Price, Paula Bolton‐Maggs, Carmel Curtis, Cindy Wakefield, Tricia A. Burke, Gustavo Dufort, Gerhard Gaedicke, Arne Riedlinger, C. Soltner, Estela Citrín, Yves Réguerre, Isabelle Pellier, Cindy Neunert, George R. Buchanan

Bibliographic record

VenueArchives of Disease in Childhood · 2013
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsChildren's Hospital of Eastern OntarioHospital for Sick ChildrenSickKids FoundationLaurentian University
Fundersnot available
KeywordsMedicineImmune thrombocytopeniaPrednisonePediatricsQuality of life (healthcare)Health related quality of lifeInternal medicinePlateletDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the impact of therapy on the reported health-related quality of life (HRQoL) in children with primary immune thrombocytopenia (ITP) using the Kids ITP tool (KIT). DESIGN: Secondary data analysis of the international and North American KIT validation studies. RESULTS: 217 children from 6 countries participated in the two studies. The majority of treatments occurred in children with newly diagnosed ITP. There was no statistical difference in age, platelet count and bleeding severity at presentation in those who physicians chose to treat or observe. Self-reported KIT scores did not differ between the two groups. The KIT parent-proxy scores were significantly worse for newly diagnosed children receiving treatment, especially following prednisone. CONCLUSIONS: Treatment of ITP does not improve, and may worsen, the HRQoL of children with ITP as measured using the KIT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.288
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2013
Admission routes1
Has abstractyes

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