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Record W4220998094 · doi:10.1097/mbc.0000000000001134

Use of thrombolytic agents to treat neonatal thrombosis in clinical practice

2022· article· en· W4220998094 on OpenAlexaff

Bibliographic record

VenueBlood Coagulation & Fibrinolysis · 2022
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsStreptokinaseUrokinaseThrombolysisDosingFibrinolysisThrombusThrombosisThrombolytic drug

Abstract

fetched live from OpenAlex

Among children, neonates have the highest incidence of thrombosis. Thrombolytic agents are used for the management of life and/or organ-threatening thrombosis. Literature on the efficacy and safety of thrombolytic agents in neonates is limited. We reviewed the evidence on dosing, administration, monitoring and treatment duration of tissue plasminogen activator (tPA), streptokinase and urokinase (URK) in neonates (≤ 28days). A systematic literature search was conducted of current databases from inception until 31 March 2021. The initial search yielded 6881 articles and 18 were retained for review. tPA, streptokinase and URK was utilized in 12, seven and four studies on 115, 51 and 16 patients, respectively. The dose range for tPA, streptokinase and URK was 0.01 -0.6 mg/kg/h, 50-2000 and 1000-0 000 units/kg/h, respectively, and treatment duration ranged from 30 min to 30 days. This is the first study to objectively summarize the efficacy and safety of thrombolytic agents in neonates. Overall, thrombolysis was associated with 87.9% complete or partial thrombus resolution and 7.4% recurrence risk. The bleeding risk associated with thrombolytic agents was 23.1% on pooled analysis, which is higher than other anticoagulants. Larger prospective studies are required to determine effective dosing regimens of these therapeutic drugs and further clarify their efficacy and safety. Blood Coagul Fibrinolysis 33:000-000 Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.112
GPT teacher head0.368
Teacher spread0.257 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2022
Admission routes1
Has abstractyes

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