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Record W3016509977 · doi:10.1186/s13023-020-01352-5

Detection of alpha-1 antitrypsin deficiency: the past, present and future

2020· review· en· W3016509977 on OpenAlexafffund
Mark Brantly, Michael Campos, Angela Davis, Jeanine D’Armiento, Kenneth W. Goodman, Miriam O’Day, John Queenan, Robert A. Sandhaus, James K. Stoller, Charlie Strange, Jeffrey Teckman, Adam Wanner

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsQueen's University
FundersAlpha-1 FoundationIronwood Pharmaceuticals, IncorporatedNational Institutes of HealthGrifolsCSL BehringShire CanadaAstraZeneca
KeywordsAlpha 1-antitrypsin deficiencyMedicineIntensive care medicineDiseasePulmonary diseaseHuman geneticsPathologyImmunologyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Most patients with alpha-1 antitrypsin deficiency remain undiagnosed and therefore do not benefit from current therapies or become eligible for research studies of new treatments under development. Improving the detection rate for AATD is therefore a high priority for the Alpha-1 Foundation. A workshop was held on June 23, 2019 in Orlando, Florida during which stakeholders from the research, pharmaceutical, and patient communities focused on the topic of alpha-1 antitrypsin deficiency detection. RESULTS: A variety of detection strategies have been explored in the past and new approaches are emerging as technology advances. Targeted detection includes patients with chronic obstructive pulmonary disease, unexplained chronic liver disease, and family members of affected individuals. Newborn screening, electronic medical record data mining, and direct-to-consumer testing remain options for future detection strategies. CONCLUSION: These meeting proceedings can serve as a basis for innovative approaches to the detection of alpha-1 antitrypsin deficiency.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.261
Teacher spread0.249 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations40
Published2020
Admission routes2
Has abstractyes

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