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Record W2900800197 · doi:10.1111/ijn.12707

Meta‐analysis of risk and protective factors for gastrointestinal bleeding after percutaneous coronary intervention

2018· review· en· W2900800197 on OpenAlexaboutno aff
Lan Wang, D. Pei, Yan‐Qiong Ouyang, Xiaofei Nie

Bibliographic record

VenueInternational Journal of Nursing Practice · 2018
Typereview
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGastrointestinal bleedingPercutaneous coronary interventionMyocardial infarctionInternal medicineCochrane LibrarySurgeryMeta-analysis

Abstract

fetched live from OpenAlex

AIM: To quantitatively analyse factors related to gastrointestinal bleeding after percutaneous coronary intervention and provide evidence for the prevention of gastrointestinal bleeding. DATA SOURCES AND REVIEW METHODS: Cochrane Library, Pubmed, Embase, and Ovid databases were searched from inception to 31 May 2018; case-control and cohort studies published in English were included. The methodological quality of each study was assessed by two independent reviewers using the Newcastle-Ottawa Scale. Meta-analysis was performed using Revman version 5.3. RESULTS: A total of 16 publications yielded data about risk factors. It was found that age older than 70 years, age (per 10-year increase), female sex, baseline anaemia, history of smoking, history of using alcohol, history of peptic ulcer disease, chronic renal failure, previous bleeding, shock, congestive heart failure, acute myocardial infarction, prior use of inotropic medications, and prior use of antithrombotic medications were positively associated with gastrointestinal bleeding. Four articles yielded data about protective factors. It was found that proton-pump inhibitor and bivalirudin therapy were negatively associated with gastrointestinal bleeding after percutaneous coronary intervention. CONCLUSION: This research found risk and protective factors which can assist in effective management of this potentially fatal complication.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.424
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.000
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 head0.097
GPT teacher head0.412
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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations11
Published2018
Admission routes1
Has abstractyes

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