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Record W164879691

Physiologic effects of first-time sitting among male patients after coronary artery bypass graft surgery.

2006· article· en· W164879691 on OpenAlexaff
Paula Holland Price

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsMount Royal University
Fundersnot available
KeywordsSupine positionMedicineSittingRepeated measures designArteryCardiologyProspective cohort studyAnesthesiaCoronary artery bypass surgeryAnalysis of varianceCatheterInternal medicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the physiologic effects of sitting among post-operative coronary artery bypass graft (CABG) male patients. METHODS: A prospective, repeated measures non-experimental design was used. Power analysis was used to calculate sample size based on pilot data from 19 subjects. Fifty-five males over the age of 18 years and having first-time CABG surgery were recruited. HR, BP, and SaO2 data were collected from the Marquette bedside monitor. SvO2 was measured by a blood gas sample from the pulmonary artery catheter. Baseline measurements were obtained on all subjects while supine in bed. Measurements were repeated immediately on sitting, after five minutes, after resuming the supine position, and again after 10 minutes. RESULTS: A repeated-measures ANOVA showed a significant time effect for HR (p < 0.001), SBP (p < 0.001), DBP (p < 0.001), MAP (p < 0.001), and SvO2 (p < 0.001), but not for SaO2. CONCLUSIONS: When post-operative CABG male patients sit on the side of the bed for the first time, they experience an increase in HR and BP. Sitting involves increased oxygen consumption as evidenced by the drop in SvO2. Most patients recover to their baseline levels within 10 minutes of returning to the supine position. Nurses must be cognizant that this routine intervention may not be innocuous. Close monitoring of patients is essential and, with some, a graduated approach to sitting should be considered.

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)0.001
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 score0.044
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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