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Record W2552174813 · doi:10.1177/0885066616678578

Active Expiration and the Measurement of Central Venous Pressure

2016· article· en· W2552174813 on OpenAlexaff
Sheldon Magder, Karim Serri, Sara Verscheure, Renée Chauvin, Peter Goldberg

Bibliographic record

VenueJournal of Intensive Care Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsRoyal Victoria HospitalUniversité de MontréalHôpital du Sacré-Cœur de MontréalMcGill University Health CentreRoyal Victoria Regional Health Centre
Fundersnot available
KeywordsExpirationMedicineCentral venous pressureAnesthesiaIntensive care medicineCardiologyInternal medicineBlood pressureRespiratory systemHeart rate

Abstract

fetched live from OpenAlex

PURPOSE: To obtain a point prevalence estimate of alterations in central venous pressure (CVP) produced by active expiration in a consecutive series of intensive care patients. METHODS: We evaluated CVP tracings taken by the nurses at their morning shift change in a consecutive series of 60 cardiac surgery and 59 noncardiac surgery patients. We also assessed change in values due to the change in transducer level. Three physicians and a nurse instructor independently reviewed the tracings and determined whether there was evidence of forced expiration and whether it was type A, defined by decreasing CVP during expiration, or type B, defined by increasing CVP during expiration. RESULTS: Agreement for CVP value was 96% between a physician and a bedside nurse. Twenty-nine percent of participants had active expiration, evenly distributed between A and B types. Active expiration was not related to the type of surgery, use of bronchodilators, and the presence of chronic obstructive lung disease or abdominal distention. Active expiration was more common in nonventilated patients and patients not receiving vasopressor drugs, suggesting they were more awake. CONCLUSION: Active expiration is common in critically ill patients. Failure to recognize it can result in important errors in the estimation of CVP and other hemodynamic measurements.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.000
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 head0.028
GPT teacher head0.270
Teacher spread0.243 · 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 designBench or experimental
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

Citations12
Published2016
Admission routes1
Has abstractyes

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