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Abstract 320: Decreasing Intraosseous Pressure and Increasing Respiratory Variability Track Fluid Volume Reduction in a Porcine Hypovolemia Model

2013· article· en· W2270822281 on OpenAlexaff
Ralph J. Frascone, Joshua G. Salzman, Peter Bliss, Alex Adams, Sandi S. Wewerka, David J. Dries

Bibliographic record

VenueCirculation · 2013
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsJ. D. Irving (Canada)
Fundersnot available
KeywordsMedicineHypovolemiaReduction (mathematics)CardiologyRespiratory systemBlood pressureFluid pressureAnesthesiaInternal medicineMechanics

Abstract

fetched live from OpenAlex

Objectives: Central venous pressure (CVP) provides an invasive and imprecise but accepted method for estimating fluid volume status. Intraosseous (IO) pressure monitoring may provide an alternative to CVP. To investigate this possibility, we measured intraosseous pressure changes in a porcine model during a hemorrhagic shock protocol. Methods: Our preparation included placement of femoral artery and central venous pressure lines. IO access was obtained by standard technique (EZ-IO, Vidacare Corporation, San Antonio, TX) to monitor pressures in the femur, humerus and tibia. All pressure signals were transmitted to a data acquisition system (Dataq Instruments,Inc., Akron, OH). To create vascular fluid volume changes, 17% of each pig’s estimated total blood volume was bled every 10 minutes until physiologic signs of hemorrhagic shock were recorded (mean arterial pressure <50 mmHg; tachycardia >150/min). To estimate fluid volume status, we tracked changes in absolute arterial, venous and IO pressures. As increasing respiratory variability is an accepted indicator of developing hypovolemia, we evaluated pressure variability due to respiration in all pressures after each 10 minute bleed period using the following formula: Δsystolic pressure/pulse pressure. Results: A hypotensive state was achieved in all six animals after bleeding each animal to 66.1±11.7% (1706.57±210 ml) of their estimated baseline fluid volume. Arterial, CVP and all IO mean pressures decreased at a constant rate. The proportional decrease pre/post bleed (mmHg) was not statistically different among all pressures: arterial 78.5→ 34.0 (-57%), humerus 17.9 → 6.5 (-64%), femur 17.1 → 9.8 (-43%), tibia 14.8 → 8.4 (-43%), CVP 6.6→ -0.9. The respiratory variability ratio increased in arterial, CVP and IO pressures to a similar extent and rate from 0.2 to 0.8 as the hypovolemic state developed. Conclusions: IO pressure decreased consistently at each IO site in this hemorrhagic shock model, indicating IO pressures can be tracked as indicators of change in fluid volume status. Increasing variability in IO pressure during the respiratory cycle was also associated with decreasing vascular fluid volume. IO pressure appears to be equivalent to CVP as an indicator of fluid volume status.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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 designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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