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A Distributed International Patient Data Registry for Hairy Cell Leukemia

2016· article· en· W2580156340 on OpenAlexaff
Leslie A. Andritsos, Michael R. Grever, Mirela Anghelina, Claire Dearden, Monica Else, James S. Blachly, Omkar Lele, Farhad Ravandi, Clive S. Zent, James B. Johnston, Versha Banerji, Francesco Forconi, Anthony D. Ho, Thorsten Zenz, Sascha Dietrich, Judit Demeter, Jacqueline C. Barrientos, Jan A. Burger, Timothy G. Call, Nicholas Chiorazzi, Daniel J. DeAngelo, Julio Delgado, Andrei Fagarasanu, Brunangelo Falini, Alessandro Gozzetti, Jeffrey A. Jones, Gunnar Juliusson, Eric H. Kraut, Robert J. Kreitman, Loree Larratt, Francesco Lauria, Gerard Lozanski, Emili Montserrat, Sameer A. Parikh, Jae H. Park, Aaron Polliack, Graeme Quest, Tadeusz Robak, Alan Saven, Tamar Tadmor, Martin S. Tallman, Constantine S. Tam, Enrico Tiacci, Xavier Troussard, Omar Abdel‐Wahab, Pier Luigi Zinzani, Christopher Heckler, Philip Payne

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of AlbertaUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
Fundersnot available
KeywordsMedicineData qualityElectronic data captureHealth informaticsDisease registryComputer scienceDiseaseFamily medicinePathologyPublic healthBusinessClinical trial

Abstract

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Abstract BACKGROUND: The study of rare diseases is limited by the uncommon nature of the conditions as well as the widely dispersed patient populations. Current rare disease registries such as the National Organization of Rare Diseases utilize centralized platforms for data collection; however because of their broad nature, these do not always capture unique, disease specific elements. Hairy Cell Leukemia (HCL) is a rare leukemia globally with approximately 900 new cases diagnosed in the US each year. The HCL Foundation undertook creation of a Patient Data Registry that collects data from multiple HCL Centers of Excellence (COE) around the globe to better understand the complications, treatment outcomes, disease subtypes, comorbid conditions, epidemiology, and quality of life of patients with HCL. METHODS: Investigators at The Ohio State University Department of Biomedical Informatics and Division of Hematology in collaboration with the HCL Foundation developed a Patient Data Registry (PDR) for the longitudinal capture of high quality research data. This system differs from other registries in that it uses a federated( rather than centralized) architecture, wherein data is queried and integrated in an on-demand manner from local registry databases at each participating site. Further, the data collected for use in the registry combines both automated exports from existing electronic health records (EHRs) as well as additional data entered via a set of web-based forms. All manually entered data comes from source documents, and data provenance spanning electronic and manually entered data is maintained via multiple technical measures. Patients may be enrolled at HCL COE, or, if they do not have access to a COE they may enroll via a web-based portal (www.hairycellleukemia.org). At this time due to regulatory requirements the web-based portal is available to US patients only. All data are de-identified (see Figure 1: De-Identification Workflow) which reduces regulatory burden and increases opportunities for data access and re-use. End users have access to data via a project-specific query portal. RESULTS: The Patient Data Registry has been deployed at The Ohio State University, Royal Marsden Hospital, and MD Anderson Cancer Center, and is undergoing deployment at the University of Rochester. Up to 25 international HCL COE may participate. In addition, US patients are actively entering the registry via the web-based portal. To date, 227 patients have been consented to the registry with 119 of these being via the web-based entry point. CONCLUSION: We created an international and web-based patient data registry which will enable researchers to study outcomes in HCL in ways not previously possible given the rarity of the disease. This work was made possible by research funding from the Hairy Cell Leukemia Foundation. Figure De-Identification Workflow Figure. De-Identification Workflow Disclosures Andritsos: Hairy Cell Leukemia Foundation: Research Funding. Anghelina:Hairy Cell Leukemia Foundation: Research Funding. Lele:Hairy Cell Leukemia Foundation: Research Funding. Burger:Pharmacyclics: Research Funding. Delgado:Gilead: Consultancy, Honoraria; Novartis/GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Roche: Consultancy, Honoraria, Research Funding; Infinity: Research Funding. Jones:AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics, LLC, an AbbVie Company: Membership on an entity's Board of Directors or advisory committees, Research Funding. Lozanski:Beckman Coulter: Research Funding; Genentech: Research Funding; Stemline Therapeutics Inc.: Research Funding; Boehringer Ingelheim: Research Funding. Montserrat:Morphosys: Other: Expert Testimony; Vivia Biotech: Equity Ownership; Gilead: Consultancy, Other: Expert Testimony; Pharmacyclics: Consultancy; Janssen: Honoraria, Other: travel, accommodations, expenses. Parikh:Pharmacyclics: Honoraria, Research Funding. Park:Genentech/Roche: Research Funding; Amgen: Consultancy; Juno Therapeutics: Consultancy, Research Funding. Robak:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding. Tam:janssen: Honoraria, Research Funding; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees. Heckler:Hairy Cell Leukemia Foundation: Research Funding. Payne:Hairy Cell Leukemia Foundation: Research Funding.

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)0.021
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.011
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.007

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 head0.035
GPT teacher head0.305
Teacher spread0.270 · 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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Published2016
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