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Record W2116726415 · doi:10.5539/gjhs.v8n5p33

Evaluation of Complications of Heart Surgery in Children With Congenital Heart Disease at Dena Hospital of Shiraz

2015· article· en· W2116726415 on OpenAlexvenueno aff
Maryam Mirzaei, Samaneh Mirzaei, Elham Sepahvand, Afifeh Rahmanian Koshkaki, Marzieh Kargar Jahromi

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)Heart diseaseCardiac surgerySurgeryCardiopulmonary bypassAnemiaInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Today, with progress in the field of congenital heart surgery, different complicated actions are done in children. These actions may be associated with several complications, especially open heart surgery in which the cardiopulmonary bypass (CPB) is used. Serious complications can be caused high morbidity and mortality rates. Present study has been performed to determine the incidence of morbidity and mortality in cardiac surgery in children. METHOD: In a cross-sectional retrospective, records of 203 patients undergoing surgery for congenital heart disease in Dena hospital during 2013-2015 were reviewed for incidence of complications. Data was analyzed by using descriptive and analytical statistics and using SPSS version 18. RESULTS: The mean age of samples was 3/65±4/47 years. The majority of samples (73/8%) were undergoing open surgery. The overall adverse cardiovascular complications were respectively, renal complications (44/3%), lung (40/3%), anemia (35/9%), heart (34/4%), gastrointestinal (17/2%), brain (14/2%), need for re-intubation of the trachea 11/3%), infection (7/8%) required reoperation (5/9%) and vascular complications (1/4%). CONCLUSION: High incidence of complications after congenital heart surgery makes necessary attention to complications and their treatment after surgery. It is necessary to apply the measures and careful monitoring of patients to minimize these effects.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.374
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 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

Citations18
Published2015
Admission routes1
Has abstractyes

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