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Record W1973986624 · doi:10.1186/1471-2466-13-47

Simvastatin decreases the level of heparin-binding protein in patients with acute lung injury

2013· article· en· W1973986624 on OpenAlexfundno aff
Daniel F. McAuley, Cecilia O’Kane, Thelma Craig, Murali Shyamsundar, Heiko Herwald, Karim Dib

Bibliographic record

VenueBMC Pulmonary Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsnot available
FundersQueen's UniversityMedical Research CouncilPublic Health Agency
KeywordsMedicineSimvastatinHeparinPharmacologyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Heparin-binding protein is released by neutrophils during inflammation and disrupts the integrity of the alveolar and capillary endothelial barrier implicated in the development of acute lung injury and systemic organ failure. We sought to investigate whether oral administration of simvastatin to patients with acute lung injury reduces plasma heparin-binding protein levels and improves intensive care unit outcome. METHODS: Blood samples were collected from patients with acute lung injury with 48 h of onset of acute lung injury (day 0), day 3, and day 7. Patients were given placebo or 80 mg simvastatin for up to 14 days. Plasma heparin-binding protein levels from patients with acute lung injury and healthy volunteers were measured by ELISA. RESULTS: Levels of plasma heparin-binding protein were significantly higher in patients with acute lung injury than healthy volunteers on day 0 (p = 0.011). Simvastatin 80 mg administered enterally for 14 days reduced plasma level of heparin-binding protein in patients. Reduced heparin-binding protein was associated with improved intensive care unit survival. CONCLUSIONS: A reduction in heparin-binding protein with simvastatin is a potential mechanism by which the statin may modify outcome from acute lung injury. TRIAL REGISTRATION: Current controlled trials: ISRCTN70127774.

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 score0.026
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.027
GPT teacher head0.277
Teacher spread0.249 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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