MétaCan
Menu
Back to cohort
Record W2093842971 · doi:10.14740/cr322w

Incidence of Gastrointestinal Bleeding After Percutaneous Coronary Intervention: A Single Center Experience

2014· article· en· W2093842971 on OpenAlexvenueno aff
Aziz ur Rehman Aziz

Bibliographic record

VenueCardiology Research · 2014
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePercutaneous coronary interventionMyocardial infarctionInternal medicineIncidence (geometry)Gastrointestinal bleedingKillip classCardiologyUnivariate analysisSingle CenterGI bleedingSurgeryMultivariate analysisEndoscopy

Abstract

fetched live from OpenAlex

BACKGROUND: Gastrointestinal (GI) bleeding is a hemorrhagic complication after percutaneous coronary intervention in patients with acute myocardial infarction. The purpose of the study is to determine predictors of GI bleeding and impact of GI bleeding on the patients undergoing percutaneous coronary intervention. METHODS: GI bleeding occurred in 6 (7.1%) of 84 patients with STEMI/NSETMI (ST-segment elevated myocardial infarction/Non ST-segment elevated myocardial infarction) undergoing primary percutaneous coronary intervention. RESULTS: Univariate analysis demonstrates that patients with GI bleeding had a significantly higher previous GI bleeding (16.66% vs. 8.6%, P < 0.001). Higher Killip classification at presentation was associated with higher incidence of GI bleeding (61% vs. 18%, P < 0.01). The use of proton pump inhibitors did not reduce the risk of GI bleeding. The GI bleeding in these patients was associated with higher mortality and morbidity in the post percutaneous coronary intervention period. CONCLUSION: Although, GI bleeding in patients with MI significantly increases mortality and morbidity, previous GI bleeding and higher Killip class are associated with higher incidence of GI bleeding. High-risk patients for GI bleeding can be identified at presentation.

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)0.001
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 score0.234
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.044
GPT teacher head0.344
Teacher spread0.300 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCardiology ResearchSame topicAntiplatelet Therapy and Cardiovascular DiseasesFrench-language works237,207