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Record W2313333243 · doi:10.1097/mca.0b013e328351556e

Pharmacokinetic modeling of the high-dose bolus regimen of tirofiban in patients with severe renal impairment

2012· article· en· W2313333243 on OpenAlexafffund
Duane B. Lakings, M.C. Janzen, David J. Schneider

Bibliographic record

VenueCoronary Artery Disease · 2012
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMedicure (Canada)
FundersMedicure
KeywordsTirofibanMedicineRenal functionPharmacokineticsRegimenBolus (digestion)UrologyPharmacologyLoading doseInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

For the treatment of patients with acute coronary syndromes in the catheterization laboratory, a high-dose bolus (HDB) regimen of tirofiban (25 µg/kg bolus, followed by an infusion of 0.15 µg/kg/min) leads to a consistent and rapid inhibition of platelet aggregation during the first hour after initiation of therapy. The objective of the present study was to use pharmacokinetic modeling to identify an appropriate dosage of tirofiban that would produce a plasma concentration-time profile in patients with severe renal impairment (creatinine clearance<30 ml/min) as similar as possible to that of the HDB regimen in patients with normal renal function. For patients with severe renal impairment, previous recommendations have been to reduce the dosage by 50%. Pharmacokinetic modeling was performed with the following sets of data: the plasma concentrations of tirofiban from patients with normal renal function who were treated with the HDB regimen of tirofiban and the plasma concentrations of tirofiban from patients with severe renal impairment who were treated with a 0.1 µg/kg/min infusion of tirofiban for 1 h. In conclusion, for patients with severe renal impairment, a 25 µg/kg bolus, followed by a 0.10 µg/kg/min maintenance infusion of tirofiban produced a plasma concentration-time profile similar to that observed with the HDB regimen of tirofiban in patients with normal renal function.

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 score0.002
Threshold uncertainty score0.285

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 head0.021
GPT teacher head0.263
Teacher spread0.242 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

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