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Laboratory issues in bleeding disorders

2006· review· en· W2002852237 on OpenAlexaff
David Lillicrap, Sukesh C. Nair, Alok Srivastava, Francesco Rodeghiero, Ingrid Pabinger, Augusto B. Federici

Bibliographic record

VenueHaemophilia · 2006
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineVon Willebrand diseaseIntensive care medicineDiseasePopulationPediatricsVon Willebrand factorPathologyInternal medicine

Abstract

fetched live from OpenAlex

The clinical history of the patient and of his/her relatives is the most important tool for making correct diagnosis of inherited or acquired bleeding disorders. Several attempts have been made by clinicians to evaluate the sensitivity and specificity of bleeding symptoms. Specific and detailed questionnaires have been designed to quantify the bleeding tendency of patients with von Willebrand's disease (VWD) and a bleeding score has been calculated. VWD is considered the most frequent inherited bleeding disorder according to population studies: however, due to the complexity of its diagnosis, the number of patients with correct diagnosis of VWD in many developing countries is relatively low and most cases remain still under- or misdiagnosed. Once bleeding history is carefully evaluated by means of a bleeding score, the laboratory workout should be organized to find out the specific defect of haemostasis responsible for bleeding. Since factors involved in haemostasis are many, the correct approach must include first level screening tests with the aim to identify the abnormal phase of haemostasis involved: then, second level tests should be focused on the specific factors within the abnormal step of haemostasis. Among many other acquired bleeding disorders related to clinical conditions or to the use of drugs, the acquired inhibitors of haemostasis are rare but should be immediately characterized by appropriate laboratory tests because they can be often life-threatening for the patients.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.341
Teacher spread0.313 · 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 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

Citations50
Published2006
Admission routes1
Has abstractyes

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