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Record W4392812839 · doi:10.53555/sfs.v10i6.2252

Nurses Knowledge Concerning Management Of Arrhythmia In CCU And ICU Unite At Cardiac Centers

2023· article· en· W4392812839 on OpenAlexvenueno aff
Muhammad Khamis Aqeel Al-Anzi, Ali Mutlaq Eid Alotaibi, Mohammed Khalid Mukhalad Alotaibi, Saad Saleh Muqbil Alharbi, Nawaf Nasser Saad Alotaibi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCardiac arrhythmiaMedicineIntensive care medicineMedical emergencyCardiologyAtrial fibrillation

Abstract

fetched live from OpenAlex

Arrhythmias are a common complication in critically ill patients admitted to Cardiac Care Units (CCUs) and Intensive Care Units (ICUs) at cardiac centers. Nurses play a crucial role in the management of arrhythmias in these units, as they are often the first responders to monitor, assess, and intervene in cases of abnormal heart rhythms. This essay examines the knowledge of nurses concerning the management of arrhythmias in CCU and ICU units at cardiac centers. The study aims to analyze the methods employed by nurses to manage arrhythmias, the results of their interventions, and discuss the implications for practice and further research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.159
GPT teacher head0.325
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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