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Abstract 4912: A snapshot of myeloproliferative neoplasms in the United States: Analysis of the “myMPN” patient registry

2019· article· en· W2955358464 on OpenAlexaff
Robyn M. Scherber, Lindsey Whyte, Michelle Woehrle, Claire Harrison, John Mascarenhas, Srđan Verstovšek, Alison R. Moliterno, Ruben A. Mesa

Bibliographic record

VenueClinical Research (Excluding Clinical Trials) · 2019
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsSnapshot (computer storage)Computer scienceMedicineDatabase

Abstract

fetched live from OpenAlex

Introduction: The myeloproliferative neoplasms (MPNs) are an uncommon type of hematologic malignancy which can be accompanied by a medically complex sequalae and severe symptom burden. Patient registries allow for the evaluation and monitoring of clinically meaningful outcomes in rare diseases over time. Although patient registries exist for MPNs, the utility of these registries has been limited by inclusion of only particular institutions and/or regions or the lack of patient reported outcomes, specifically symptoms and quality of life. In September 2017, the “myMPN” patient registry began enrollment as the first MPN patient-centered registry. The purpose of this analysis is to report patient-reported disease features, outcomes, and events uploaded to the registry to date.Methods: The “myMPN” patient registry was created by the MPN Research Foundation’s steering committee and hosted on the Genetic Alliance registry platform. Utilizing previous questions created for MPN populations and validated assessment tools, the myMPN patient registry allows patients to input data disclosures, disease features, treatments, blood counts and symptoms. The registry has been granted independent IRB approval.Results: Accrual: To date, the registry has 744 participants. Of these, 62% were female and mean age was 61 years (range 18-94). Disease-related information: The registry includes 38% essential thrombocythemia (ET) patients, 36% polycythemia vera (PV) patients, 23% myelofibrosis (MF) patients, and 3% patients who reported an alternative MPN diagnosis. 11.5% of patients were not aware of their mutation status. Disease events: Over the year since study initiation, there have been 2,100 reported disease-related events, which have included 825 blood draws, 298 phlebotomies, 207 MPN medication changes, 144 bone marrow biopsies, 77 transfusions, 39 thrombotic or bleeding events, and 30 genetic testing events. Since registry initiation, 6 patients reported a new ET to MF transformation, and 4 patients reported a PV to MF transformation. Disease Symptom Burden: To date, 400 patients have completed 675 independent symptom assessments. Many have completed two or more symptom assessments. In general, MPN-10 symptom scores are similar to previously published cohorts, but provide data on longitudinal symptom change.Conclusions: The myMPN patient registry facilitates the research and care of MPN patients by clinicians, researchers, patients, patient-advocates, and caregivers a common platform to interface prospectively. Future goals of the registry are to 1) explore variables related to disease progression/transformation, 2) expand outside of the United states to other English-speaking countries, 3) allow patients to connect with their physicians regarding their registry information, and 4) to develop a compendium medical record and specimen registry.Citation Format: Robyn M. Scherber, Lindsey Whyte, Michelle Woehrle, Claire Harrison, John Mascarenhas, Srdan Verstovsek, Alison Moliterno, Ruben A. Mesa. A snapshot of myeloproliferative neoplasms in the United States: Analysis of the “myMPN” patient registry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4912.

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)0.005
metaresearch head score (Gemma)0.015
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.008
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.555
GPT teacher head0.599
Teacher spread0.044 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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