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Record W2789445901 · doi:10.1007/s10545-018-0141-z

Transplantation as disease modifying therapy in adults with inherited metabolic disorders

2018· review· en· W2789445901 on OpenAlexaff
Sandra Sirrs, Fady Hannah‐Shmouni, Stephen H. Nantel, James Neuberger, Eric M. Yoshida

Bibliographic record

VenueJournal of Inherited Metabolic Disease · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsSickKids FoundationHospital for Sick ChildrenBC Cancer AgencyUniversity of TorontoUniversity of British Columbia
FundersAstellas PharmaGilead SciencesSanofi
KeywordsTransplantationDiseaseMedicineHematopoietic stem cell transplantationNatural historyIntensive care medicinePediatricsInternal medicine

Abstract

fetched live from OpenAlex

Transplantation is an established disease modifying therapy in selected children with certain inherited metabolic diseases (IMDs). Transplantation of hematopoietic stem cells or solid organs can be used to partially correct the underlying metabolic defect, address life threatening disease manifestations (such as neutropenia) or correct organ failure caused by the disease process. Much less information is available on the use of transplantation in adults with IMDs. Transplantation is indicated for the same IMDs in adults as in children. Despite similar disease specific indications, the actual spectrum of diseases for which transplantation is used differs between these age groups and this is partly related to the natural history of disease. There are diseases (such as urea cycle defects and X-linked adrenoleukodystrophy) for which transplantation is recommended for selected symptomatic patients as a treatment strategy in both adults and children. In those diseases, the frequency with which transplantation is used in adults is lower than in children and this may be related in part to a reduced awareness of transplantation as a treatment strategy amongst adult clinicians as well as limited donor availability and allocation policies which may disadvantage adult patients with IMDs. Risks of transplantation and disease-specific prognostic factors influencing outcomes also differ with age. We review the use of transplantation as a disease modifying strategy in adults focusing on how this differs from use in children to highlight areas for future research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations10
Published2018
Admission routes1
Has abstractyes

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