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Record W2464396699 · doi:10.1080/17474086.2016.1208555

Hematological disorders: a commonly unrecognized cause of acute stroke

2016· review· en· W2464396699 on OpenAlexfundno aff
Adrià Arboix, Carme Jiménez, Joan Massons, Olga Parra, Carles Besses

Bibliographic record

VenueExpert Review of Hematology · 2016
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsnot available
FundersHeart and Stroke Foundation of CanadaAmerican Heart Association
KeywordsMedicineStroke (engine)EtiologyDiseaseIntensive care medicineVenous thrombosisComplicationThrombosisPediatricsVascular diseaseInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Hematological disorders account for about 1.3% of all causes of acute stroke. This systematized review presents updated information on the implications of this category of heterogeneous diseases as a cause of stroke. AREAS COVERED: The most relevant aspects of the relationship between stroke and hematological disorders are reported. A high index of suspicion is needed in young stroke patients, patients with recurrent stroke of undetermined cause, and in patients with prior history of venous thrombosis to identify a potential hematological disorder as the definitive etiology of stroke Expert commentary: Stroke can be the presenting manifestation of a specific hematological disease or may appear as a complication in the course of hematological disorders. It is important to make a correct diagnosis of the underlying hematological disorder in order to treat stroke patients promptly and appropriately as well as to establish the optimal secondary prevention strategy for recurrent vascular cerebral disease.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.049
GPT teacher head0.408
Teacher spread0.359 · 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 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

Citations88
Published2016
Admission routes1
Has abstractyes

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