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Record W2169442038 · doi:10.1136/heartjnl-2014-306713

Incidence and predictors of cardiac catheterisation-related arterial thrombosis in children

2015· article· en· W2169442038 on OpenAlexaff
Barbara Brotschi, Maja I. Hug, Oliver Kretschmar, Mattia Rizzi, Manuela Albisetti

Bibliographic record

VenueHeart · 2015
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineCardiac catheterisationIncidence (geometry)ThrombosisCardiologyInternal medicineInterventional cardiology

Abstract

fetched live from OpenAlex

OBJECTIVE: Arterial thrombosis is one of the most reported complications of cardiac catheterisation (CC) in children. The aim of the study was to evaluate the incidence and predictors of arterial thrombosis in children with cardiac diseases (CDs). METHODS: During 12 consecutive months, all children aged 0-19 years undergoing CC of the femoral arteries were included in this observational study. After CC, clinical evaluation of impaired limb perfusion was performed according to local guidelines. Doppler ultrasonography was performed when decreased limb perfusion was suspected. RESULTS: 123 children (30% aged <12 months, 70% aged >12 months) underwent CC. Arterial thrombosis occurred in 14 of the 123 children (11.4%). Twelve cases (12/14=86%) of arterial thrombosis occurred in infants aged <12 months and 2 (2/14=14%) in older children. Overall younger age (p<0.01, OR (95% CI) 0.49 (0.28 to 0.86)) and low body weight (p<0.004, OR (95% CI) 0.78 (0.65 to 0.92)) were significantly associated with an increased risk of arterial thrombosis. Cyanotic CD (p=0.07, OR (95% CI) 2.87 (0.90 to 9.15)) showed a trend towards increased thrombotic risk. CONCLUSIONS: Arterial thrombosis is a common complication of CC in infants. Diagnosis of CC-related arterial thrombosis remains a challenge. Well-defined clinical monitoring protocols may be valuable methods for timely detection and treatment of arterial thrombosis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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 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

Citations37
Published2015
Admission routes1
Has abstractyes

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