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Record W2765341815 · doi:10.14740/cr582e

In-Hospital Clinical Outcomes and Procedural Complications of Percutaneous Coronary Intervention in Elderly Patients

2017· article· en· W2765341815 on OpenAlexvenueno aff
Seyed Fakhreddin Hejazi, Leili Iranirad, Kobra Doostali, Narges Khodadadi, Sameeye Norouzi

Bibliographic record

VenueCardiology Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsnot available
FundersQom University of Medical Science
KeywordsMedicinePercutaneous coronary interventionCardiologyInternal medicineIntervention (counseling)PercutaneousMyocardial infarctionNursing

Abstract

fetched live from OpenAlex

BACKGROUND: As population growth leads to an increase in the number of the elderly with coronary artery disease, an evaluation of the clinical outcomes of percutaneous coronary intervention (PCI) in the elderly patients seems to be essential. METHODS: A prospective, observational cohort study was performed on 468 patients in two groups of elderly and non-elderly patients (mean age: 60.01 ± 10.84 years; ≥ 70 years, 20.1%; men, 62%) who underwent PCI, to evaluate the procedural success and in-hospital major and minor adverse cardiovascular events in the elderly patients. RESULTS: The procedural success rate was significantly lower (95.7% vs. 99.5%, P = 0.017) and the rates of in-hospital complications were significantly higher (10.6% vs. 0.8%, P < 0.0001) in elderly (+70) than in non-elderly patients. On the basis of a multivariate analysis, being elderly was not an independent predictor of procedural failure, but increased the chance of in-hospital complications to 8% higher (odds ratio: 0.08; 95% confidence interval: 0.01 - 0.39; P = 0.002). CONCLUSION: Regardless of the difference in the procedural success and in-hospital complication rates between our two study groups, aging is not an important predictor of them. Furthermore, PCI should not be refused in elderly patients if indicated.

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)0.003
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 score0.009
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.089
GPT teacher head0.463
Teacher spread0.374 · 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
Published2017
Admission routes1
Has abstractyes

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