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Record W3135801617 · doi:10.12968/bjon.2021.30.4.230

Central venous pressure monitoring in critical care settings

2021· article· en· W3135801617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsCentral venous pressureMedicineCentral venous catheterResuscitationSubclavian veinIntensive care medicineSuperior vena cavaHemodynamicsCritically illCatheterInternal jugular veinCardiologyAnesthesiaSurgeryBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Patients who present with acute cardiovascular compromise require haemodynamic monitoring in a critical care unit. Central venous pressure (CVP) is the most frequently used measure to guide fluid resuscitation in critically ill patients. It is most often done via a central venous catheter (CVC) positioned in the right atrium or superior or inferior vena cava as close to the right atrium as possible. The CVC is inserted via the internal jugular vein, subclavian vein or via the femoral vein, depending on the patient and their condition. Complications of CVC placement can be serious, so its risks and benefits need to be considered. Alternative methods to CVC use include transpulmonary thermodilution and transoesophageal Doppler ultrasound. Despite its widespread use, CVP has been challenged in many studies, which have reported it to be a poor predictor of haemodynamic responsiveness. However, it is argued that CVP monitoring provides important physiologic information for the evaluation of haemodynamic instability. Nurses have central roles during catheter insertion and in CVP monitoring, as well as in managing these patients and assessing risks.

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.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.351
Teacher spread0.326 · 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