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Record W2411498720 · doi:10.1159/000457546

Effect of Indomethacin onCerebral Blood Flow Velocities inVery Low Birth Weight Neonateswith a Patent Ductus Arteriosus

2017· article· en· W2411498720 on OpenAlexaff
Arne Ohlsson, Jean Bottu, J. Govan, M.-L. Ryan, Katherine Fong, Terri L. Myhr

Bibliographic record

VenueDevelopmental Pharmacology and Therapeutics · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineDuctus arteriosusMiddle cerebral arteryHemodynamicsCerebral blood flowAnesthesiaBlood flowBlood pressureDiastolePolycythaemiaCardiologyInternal medicineIschemia

Abstract

fetched live from OpenAlex

The effect of repeated doses of indomethacin on mean peak velocity (MPV) and time-averaged mean velocity in the middle cerebral artery was assessed in 10 ventilated neonates with a patent ductus arteriosus using colour/duplex Doppler technique prior to, and 10, 30, and 120 min after the first and the third dose. Velocities were significantly reduced up to 120 min after the first dose. The third dose resulted in a significant reduction in MPV at 10 and 30 min following treatment. This reduction was half of that observed after the first dose. Systemic blood pressure (BP) and heart rate did not change significantly after each separate dose. However, by the third dose, mean and diastolic BP were significantly increased from pretreatment levels. The attenuated response of cerebral blood flow (CBF) velocities to the third dose of indomethacin compared with the first dose is probably related to altered haemodynamics. Indomethacin should be used cautiously in infants with other conditions which are known to decrease CBF such as hypotension, hypocarbia and polycythaemia.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.277
Teacher spread0.259 · 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 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

Citations52
Published2017
Admission routes1
Has abstractyes

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