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Record W3097833703 · doi:10.1182/blood-2020-140684

The Importance of Hematology Working Groups for Rare Genetic Diseases

2020· article· en· W3097833703 on OpenAlexaff
Marwan ElBagoury, Ohoud F Kashari

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsSanofi (Canada)
Fundersnot available
KeywordsMedicineIntensive care medicineRare diseaseEtiologyPediatricsDiseasePathology

Abstract

fetched live from OpenAlex

Rationale Around the globe, it is now understood that individuals with Rare genetic Diseases routinely face limitations to getting access to diagnosis. Plans have been created to improve the requirements of the patient's communities, including access to multidisciplinary care, and proposing new corrections or amendments to existing strategies. In the gulf region, numerous proposals have been established to tackle the diagnosis and management Rare genetic Diseases. Introduction and Background Rare genetic diseases are characterized as life-long, serious conditions that debilitate or compromise life. Almost 80% of Rare genetic diseases are diagnosed during the childhood. Absence of access to these assets affect patients and their families living with complex needs that may incorporate day in and day out observing, continuous serious physical and formative medicines, remaining in the training framework, and now and then costly strength meds1. The underlying etiology may stay obscure for many patients with rare genetic diseases despite multiple investigations. patients may be assigned an incorrect diagnosis and be referred to several specialties until a correct diagnosis can be made. A correct diagnosis of rare genetic diseases may impact not only the patient's care but may have further implications for management and/or counselling of family members as well2. Also, Early diagnosis leading to early treatment to prevent long-term damage. Global Landscape3 Rarity of diseases is most commonly defined based on prevalence and incidence within a jurisdiction, or in some cases by a combination of factors based on severity and the existence or feasibility of alternative therapeutic options. Globally, the following areas of focus aimed at improving the delivery of health care for the rare disease population: - Improve access to early diagnosis, timely intervention, coordinated care for rare genetic disease patients and developing referral pathways for rare genetic disease patients to facilitate efficient care deliver - Provide educational resources and knowledge exchange opportunities to health professionals to allow them to better identify, manage and treat rare disea - support integrated peer networks, patient organizations to ensure that rare disease patients, their family/caregivers and support them to make informed decisions about their condition. The importance of having working groups for Rare genetic Diseases in Gulf region 4 - Encourage improved coordination of care and access to particular information for rare genetic diseaseses. - Create a complete system services suppliers over Gulf states. Assets and Gaps analysis 1- Early Detection and Diagnostics 5 There are resources that assist the diagnostic capacity and early detection for rare genetic diseases. · Whole Exome sequencing are used mainly for research purposes, despite the fact that their use will reduced diagnostic odyssey. · Lack of the availability of testing is dependent on budget support in some hospitals. - Timely Access to Evidence-based care 6 - Family doctors may not be well equipped to meet the needs of patients with rare hematological genetic diseases, even after diagnosis. - Poor access supportive services for adult care. - Access to genetic counseling for patients and families outside major academic hospitals7. References 1. Sawyer, S. L. et al. Utility of whole-exome sequencing for those near the end of the diagnostic odyssey: time to address gaps in care. Clin. Genet.89, 275-284 (2016). 2. Undiagnosed Diseases Network Manual of Operations. (2018). 3. Richter, T. et al. Rare Disease Terminology and Definitions-A Systematic Global Review: Report of the ISPOR Rare Disease Special Interest Group. (2015). doi:10.1016/j.jval.2015.05.008 4. International Rare Disease Research Consortium& GUIDELINES Long version. (2013). 5. Clinical Handbook for Sickle Cell Disease Vaso-occlusive Crisis Provincial Council for Maternal and Child Health & Ministry of Health and Long-Term Care. (2017). 6. Therrell, B. L. et al. Current status of newborn screening worldwide: 2015. Seminars in Perinatology39, 171-187 (2015). 7. Stille, C. J. & Antonelli, R. C. Coordination of care for children with special health care needs. Current Opinion in Pediatrics16, 700-705 (2004). Figure 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)0.028
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.085
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0060.006
Open science0.0030.008
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0500.012

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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