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Strategies of Screening and Treating Post-extubation Dysphagia: An Overview of the Situation in Greek-Cypriot ICUs

2023· preprint· en· W4380028058 on OpenAlexaff
Meropi Mpouzika, Stelios Iordanou, Maria Kyranou, Katerina Iliopoulou, Stelios Parissopoulos, Maria Kalafati, Elizabeth Papathanassoglou

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDysphagiaMedicineIntensive care unitSwallowingIntensive careIntensive care medicineProtocol (science)MEDLINEPhysical therapyAlternative medicineSurgeryPathology

Abstract

fetched live from OpenAlex

Post-extubation dysphagia (PED) can lead to serious health problems in critical ill patients, yet routine bedside screening may be lacking in many Intensive Care Units (ICUs), possibly due to limited awareness for this condition. This study aimed to establish baseline data on the current approaches, the status of perceived best practices to PED screening and treatment, as well as to assess awareness of PED. A nationwide cross-sectional, online survey was conducted of all adult ICUs in the Republic of Cyprus in June 2018. More than 85% of ICUs reported that there was no standard protocol indicating which patients should be screened for PED. Cough reflex testing and water swallow test were the most reported assessment methods used to confirm the presence of PED. Muscle strengthening exercises without swallowing and swallowing exercises were mostly used to treat dysphagia. Overall, 28.6% of the ICUs agreed that PED was common in their unit. We identified gaps in Greek Cypriot ICUs awareness and knowledge regarding PED screening and treatment. Comprehensive unit-based dysphagia education programs must be urgently im-plemented and interdisciplinary and collaborative work between nurses, intensivists and speech and language therapists is needed to address the situation and improve the quality of care pro-vided.

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)0.002
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 score0.008
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.362
GPT teacher head0.498
Teacher spread0.136 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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