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Second Line ITP Therapy in the UK: Results from the UK Paediatric ITP Registry

2015· article· en· W2575937652 on OpenAlexaff
Jenna Lakhani, Robert J. Klaassen, Nicholas Barrowman, Jason Chan, John D. Grainger

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineSplenectomyRituximabPediatricsImmune thrombocytopeniaAzathioprineThrombopoietinDapsoneSecond line treatmentQuality of life (healthcare)Internal medicinePlateletChemotherapyDiseaseLymphomaImmunology

Abstract

fetched live from OpenAlex

Abstract Background: Immune thrombocytopaenia (ITP) is the most common acquired bleeding disorder in children and is typically a self-limiting condition that resolves within six months. Although a 'watch and wait' approach is commonly used in the management of paediatric ITP, treatment options are available to aid in managing complex or severe cases, with the ultimate goal being to maintain health-related quality of life (HRQL) and avoid serious complications. This study aims to understand the use and indications for second-line treatments in persistent and chronic ITP (duration of 3 months or longer) based on an analysis of the UK Paediatric ITP Registry. Methods: The UK Paediatric ITP Registry is a prospective database of children presenting with ITP to over 100 paediatric treatment centres in the UK between 2005 and 2015. Patients with ITP were included if they received any second line ITP treatment: specifically rituximab, thrombopoietin receptor agonists (TPO-RAs), azathioprine, dapsone and splenectomy. Factors including age, sex, bleeding episodes and severity as well as follow-up platelet counts were analysed. Patients receiving second line treatments were then compared to similar patients who did not receive treatment but who also had persistent (> 3 months) or chronic (> 12 months) ITP and at least one platelet count of 30x109/l or less during that time period. Results: Of 938 patients in the database, 537 were identified as having persistent or chronic ITP. 22 of these patients received a form of second line treatment. This is 4% of all persistent and chronic ITP patients and 16% of those with at least one recorded platelet count <30x109/l. Of the patients receiving second line treatment, 77% were female and 59% were age 10 or older when diagnosed, which when compared to the control group, show age (p=0.002) and sex (p=0.019) as significant factors in the use of second line treatment. Bleeding episodes and severity were similar between both groups with 64% of patients in each group having at least one episode of moderate or severe bleeding. Of the 22 patients receiving second line treatment, 59% showed a marked improvement in platelet counts after one course. 32% showed no significant response despite multiple courses (Figure). Conclusion: In the UK Paediatric ITP Registry, teenage girls were more likely to be prescribed second line therapy (50% were females aged 10 or older), and the majority of patients showed a marked improvement in their platelet count with one course of therapy. Future research should look at the impact of TPO-RAs as they become licensed and HRQL outcomes to help select which ITP patients would benefit the most from second line therapy. Figure 1. Graphic representation of platelet counts throughout course of ITP in patients receiving second line treatments using a logarithmic scale Figure 1. Graphic representation of platelet counts throughout course of ITP in patients receiving second line treatments using a logarithmic scale Disclosures No relevant conflicts of interest to declare.

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)0.012
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 score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.264
Teacher spread0.232 · 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".

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Citations1
Published2015
Admission routes1
Has abstractyes

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