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Record W2014438317 · doi:10.1161/01.str.32.1.262

Venous Thromboembolism After Acute Stroke

2001· review· en· W2014438317 on OpenAlexaff
James Kelly, Anthony Rudd, Russell E. Lewis, Beverley J. Hunt

Bibliographic record

VenueStroke · 2001
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicinePulmonary embolismVenous thromboembolismStroke (engine)Intensive care medicineSubclinical infectionHeparinEmbolismThrombosisSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Treatment for venous thromboembolism (VTE) is highly effective in preventing morbidity and mortality, yet pulmonary embolism (PE) accounts for up to 25% of early deaths after stroke. This is because the current diagnostic paradigm is reactive rather than proactive: the clinician responds to VTE when it becomes symptomatic, in the expectation that initiation of treatment will prevent progression to more serious manifestations. This approach is flawed, because sudden death from PE is frequently unheralded and nonfatal symptomatic pulmonary emboli are often unrecognized or misdiagnosed. SUMMARY OF COMMENT: Morbidity and mortality from PE could be reduced either by more effective thromboprophylaxis or earlier diagnosis and treatment of established VTE. The fact that early use of short-term, low-dose, unfractionated heparin (UFH) is not associated with sustained, clinically meaningful benefit suggests that a fundamental change in the diagnostic approach to VTE is needed, one which requires a greater appreciation that clinically apparent events are merely the tip of the thromboembolism iceberg. CONCLUSIONS: Research into a strategy of screening for subclinical VTE in these patients is needed, with a view to identifying a subgroup at risk of progression to symptomatic and life-threatening events, in whom outcome might be improved by anticoagulation.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.030
GPT teacher head0.329
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

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

Citations269
Published2001
Admission routes1
Has abstractyes

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