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The Mcmaster ITP Registry: Assessing the Prevalence, Clinical and Laboratory Features of Immune Thrombocytopenia

2014· article· en· W2415416350 on OpenAlexaff
Donald M. Arnold, Rumi Clare, Mary Salib, Robert Clayden, Grace Wang, Ishac Nazi, John G. Kelton

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcMaster UniversityCanadian Blood Services
Fundersnot available
KeywordsMedicineInternal medicineCohortProspective cohort studyEpidemiologyObservational studyPediatricsAutoantibodyCohort studyImmune thrombocytopeniaImmunologyPlateletAntibody

Abstract

fetched live from OpenAlex

Abstract Introduction: Immune thrombocytopenia (ITP) is a common platelet disorder; however, it is a heterogeneous disease and optimal treatment has not been established. Understanding the epidemiology of ITP requires large observational cohort studies and prospective registries with prolonged follow-up. We established the McMaster ITP Registry to study the natural history of ITP and to identify clinical and laboratory features that may distinguish disease subgroups. The objectives of this study were 1) to assess the accuracy of data collection in the McMaster ITP Registry; and 2) to describe the prevalence, clinical features and platelet autoantibody results from a large cohort of ITP patients. Methods: The McMaster ITP Registry enrolls consecutive adult patients with thrombocytopenia (platelet count <150 x109/L) from a tertiary hematology clinic. Patients are prospectively followed every 6 months until discharge or death. Patients are assigned to a diagnostic category based on information from the most recent clinic visit. Baseline and time-varying characteristics are collected including prevalent and incident bleeding events and treatments received. Laboratory tests, including screening for secondary causes, are performed at baseline and all platelet counts measured during follow up are captured. Platelet autoantibody testing for anti-glycoprotein (GP) IIbIIIa and anti-GP IbIX is performed at baseline, 6 and 12 months using the direct antigen capture method. Accuracy of data capture for diagnosis, disease stage of ITP, and lowest platelet count was evaluated for 50 registry patients chosen at random by comparing the data in the registry with data abstracted from patients’ charts by 2 independent assessors. Agreement was calculated using Cohen’s kappa (k). Funding for the registry was provided by Amgen. Results: From January 2010 to February 2014, 465 thrombocytopenic patients were enrolled in the McMaster ITP Registry: 258 (55.5%) had ITP, either primary (n=221) or secondary (n= 37). The remaining 207 patients (44.5%) had non-immune thrombocytopenia associated with pregnancy, myelodysplastic syndrome, liver disease or other causes. Median age at diagnosis of ITP was 41 years [interquartile range (IQR), 32 – 58], 62.8% were female and patients had received a median of 2 (IQR, 0 – 4) treatments at last follow up. 33.3% of patients had splenectomy, 15.4% had received rituximab and 20.5% had received thrombopoietin receptor agonists (either romiplostim or eltrombopag). Platelet antibodies were measured in 197 patients with primary ITP: 109 (55.3%) had either anti-IIbIIIa or anti-IbIX. Accuracy of data collection was excellent for all items checked (k>0.8 for each); yet, to improve the method of capturing diagnosis and disease stage, we removed a category (‘mild thrombocytopenia’), renamed a category (‘liver disease’) and added a category (‘unknown cause’) following this validation exercise. Conclusion: In the setting of a tertiary hematology referral clinic, 55% of patients presenting with thrombocytopenia had ITP. Of patients with primary ITP, 55.3% had anti-platelet autoantibodies. Our classification of patients by diagnosis of thrombocytopenia was simplified after the validation study. The McMaster ITP Registry can help identify clinical and laboratory features of ITP patients to better understand natural history and treatment responses. Disclosures Arnold: GSK: Honoraria, Research Funding; Hoffman-LaRoche: Research Funding; Bristol Myers Squibb: Consultancy; Amgen: Consultancy, Honoraria, Research Funding.

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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)0.001
metaresearch head score (Gemma)0.006
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 score0.140
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.317
Teacher spread0.301 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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