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Record W2027538442 · doi:10.1111/ijlh.12078

Molecular testing for disorders of hemostasis

2013· review· en· W2027538442 on OpenAlexafffund
David Lillicrap

Bibliographic record

VenueInternational Journal of Laboratory Hematology · 2013
Typereview
Languageen
FieldMedicine
TopicHemophilia Treatment and Research
Canadian institutionsQueen's University
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsHemostasisGenetic testingPhenocopyMedicinePopulationPrenatal diagnosisMedical diagnosisGenetic counselingFamily historyIntensive care medicinePhenotypeGeneticsSurgeryPathologyInternal medicineBiologyGenePregnancy

Abstract

fetched live from OpenAlex

The investigation of inherited bleeding disorders with routine tests of hemostasis will yield clear diagnostic information in the majority of subjects with an unequivocal history of bleeding and especially in those where the phenotypic severity is severe and where an obvious family history of bleeding is present. Nevertheless, a significant minority of subjects with obvious bleeding symptoms will remain without a definite diagnosis after extensive hemostatic testing. With these facts in mind, the role of molecular testing for inherited disorders of hemostasis now includes the following: confirmation of a phenotypic diagnosis through targeted genetic analysis, the distinction of bleeding phenocopies by molecular analysis, and provision of genetic testing as the investigation of choice in situations such as prenatal diagnosis and detection of the carrier state for inherited bleeding traits. In addition, molecular testing can sometimes be used to provide supplementary knowledge that can be used to enhance clinical care. Finally, the utility of genome-wide approaches to identify novel genetic associations may provide new information to explain the cause of bleeding in the population of bleeders without established diagnoses.

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)0.001
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 score0.980
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
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 head0.074
GPT teacher head0.423
Teacher spread0.349 · 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

Citations6
Published2013
Admission routes2
Has abstractyes

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