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Validation Study of PulseCO System for Continuous Cardiac Output Measurement

2005· article· en· W2046181593 on OpenAlexaff
George Berberian, T. Alexander Quinn, Deon W. Vigilance, David Y. Park, Santos E. Cabreriza, Lauren J. Curtis, Henry M. Spotnitz

Bibliographic record

VenueASAIO Journal · 2005
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsColumbia College
FundersNational Heart, Lung, and Blood Institute
KeywordsCardiac outputCardiologyInternal medicineBiomedical engineeringComputer scienceMedicineHemodynamics

Abstract

fetched live from OpenAlex

Ultrasonic flow probes have been used to optimize biventricular pacing immediately after cardiopulmonary bypass, improving cardiac output (CO) by 10%; however, flow probes must be removed with chest closure. The PulseCO system (LiDCO Limited, Cambridge, UK) may extend optimization into the postoperative period, but controlled validations have not been reported. Six anesthetized pigs were instrumented for right heart bypass. Flow was varied from 3 to 1 L/min and then back to 3 in 0.5 L/min increments for 60 second intervals. CO was measured by ultrasonic flow probe on the aorta and by PulseCO using a femoral arterial line. PulseCO and flow probe accurately measured CO (PulseCO R2: 0.79-0.95; flow probe R2: 0.96-0.99). At flow of 2 L/min, when the heart was paced 30 bpm over the sinus rate, PulseCO falsely indicated an increase in CO (2.13 vs. 2.30 L/min, p = 0.014). When mean arterial pressure was increased by 20% using a phenylephrine infusion, PulseCO falsely indicated an increase in CO (2.13 vs. 2.47 L/min, p = 0.014). When mean arterial pressure was decreased by 20% using a nitroprusside infusion, PulseCO falsely indicated a decrease in CO (2.13 vs. 1.79 L/min, p = 0.003). PulseCO appears to be useful for assessing acute changes in CO if its limitations are recognized.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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 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

Citations25
Published2005
Admission routes1
Has abstractyes

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