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Record W4411345152 · doi:10.3390/jcm14124279

Incidence and Risk Factors of Dysphagia After Cardiac Surgery: A Scoping Review

2025· review· en· W4411345152 on OpenAlexaboutno aff
Christos Kourek, Emilia Michou, Stavros Dimopoulos

Bibliographic record

VenueJournal of Clinical Medicine · 2025
Typereview
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDysphagiaIncidence (geometry)Intensive care medicineCardiac surgeryGeneral surgeryInternal medicineSurgery

Abstract

fetched live from OpenAlex

Dysphagia is a serious complication following cardiac surgery, associated with increased morbidity, prolonged hospitalization, and higher healthcare costs. Variability in the incidence and risk factors highlights the need for consolidated evidence. This scoping review aimed to analyze the incidence of dysphagia after cardiac surgery and identify the associated risk factors. A search was conducted in the PubMed, Embase, Web of Sciences, and PEDro databases for observational studies reporting dysphagia incidence and risk factors in adult cardiac surgery patients. The Newcastle-Ottawa Scale was used to assess the studies' quality and out of 2920 studies identified, 15 met the inclusion criteria for inclusion in this review. Dysphagia incidence ranged from 2.7% to 60%, with higher rates observed when objective assessments such as FEES or VFSS were employed. Key risk factors included advanced age, prolonged intubation, cerebrovascular events, and complex operative procedures. Post-operative dysphagia was linked to complications like aspiration pneumonia, prolonged ICU/hospital stays, and increased healthcare costs. In conclusion, dysphagia is a significant but under-recognized complication of cardiac surgery. Advanced age, prolonged intubation, and surgical complexity are major risk factors. Standardized assessment protocols and early interventions are crucial to mitigating its impact and improving patient clinical outcomes.

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)0.020
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.205
GPT teacher head0.597
Teacher spread0.392 · 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.

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

Citations3
Published2025
Admission routes1
Has abstractyes

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