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Record W2037374715 · doi:10.1155/2009/241376

Aminoterminal Pro B-Type Natriuretic Peptide (NT-proBNP) Levels for Monitoring Interventions in Paediatric Cardiac Patients with Stenotic Lesions

2009· article· en· W2037374715 on OpenAlexaff
Eva Welisch, K Kleesiek, Nikolaus Haas, Kambiz Norozi, R. Rauch, Guido Filler

Bibliographic record

VenueInternational Journal of Pediatrics · 2009
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineNatriuretic peptideInternal medicineCardiologyPsychological interventionHeart diseaseIntervention (counseling)PediatricsHeart failure

Abstract

fetched live from OpenAlex

Background. Serum concentration of NT-proBNP correlates well with the severity of cardiac disease in adults. Few studies have been performed on the applicability of NT-proBNP for monitoring children with congenital heart disease. Objective. To assess the potential of NT-proBNP for monitoring the success of interventions in children with stenotic cardiac lesions. Methods. NT-proBNP was measured in 42 children aged 1 day to 17 years (y) before and 6 to 12 weeks after surgical or interventional correction of obstructive lesions of the heart. Comparison is made with the clinical status and echocardiographic data of the child. Results. NT-proBNP levels (median 280, range 10-263,000 pg/mL) were above the reference value in all but 6 patients (pts) prior to the intervention. Higher levels were found in more compromised patients. The 35 children with clinical improvement after the procedure showed a decline of their NT-proBNP level in all but 4 patients, whose levels remained unchanged. Five patients with unchanged gradients despite a therapeutic intervention also demonstrated unchanged NT-proBNP levels after the intervention. Thus, the success rate of the procedure correlated well to clinical and echocardiographic findings. Conclusion. NT-proBNP can be used to assess the efficiency of an intervention.

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 score0.030
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.042
GPT teacher head0.330
Teacher spread0.288 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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