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Record W2801875570 · doi:10.1080/14779072.2018.1475231

Primary prevention of post-pericardiotomy syndrome using corticosteroids: a systematic review

2018· review· en· W2801875570 on OpenAlexaff
Rachel Wamboldt, Gianluigi Bisleri, Benedict M. Glover, Sohaib Haseeb, Gary Tse, Tong Liu, Adrián Baranchuk

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2018
Typereview
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePerioperativeIntensive care medicineRegimenPopulationSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Post-pericardiotomy syndrome is a well-recognized inflammatory phenomenon that commonly occurs in patients following cardiac surgery. Due to the increased morbidity and resource utilization associated with this condition, research has recently focused on ways of preventing its prevention this condition; primarily using colchicine, NSAIDs and corticosteroids. Areas covered: This systematic review summarizes the three clinical studies that have used corticosteroids for PPS primary prevention in the perioperative period. Due to the heterogeneity amongst these three studies in terms of population (both pediatric and adult patients), surgical procedure, administration regimen and results (only 1/3 studies reporting a positive effect), the effectiveness of corticosteroids remains unproven. Expert commentary: Corticosteroids have shown to be useful in the treatment of PPS but have thus far have shown mixed results as a primary prevention method. Research on patients taking corticosteroids pre-operatively have shown a significant reduction in the risk of developing PPS. Further research is required to determine if corticosteroids are helpful in preventing PPS in patient undergoing cardiac surgery, before any recommendations regarding their use in cardiovascular surgery can be made.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.449
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0260.027
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.355
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2018
Admission routes1
Has abstractyes

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