MétaCan
Menu
Back to cohort
Record W2086511844 · doi:10.1186/cc5454

Lithium dilution cardiac output measurement in the critically ill patient: determination of precision of the technique

2007· article· en· W2086511844 on OpenAlexfundno aff
Maurizio Cecconi, Nawaf Al-Subaie, Matias Cañete, Deborah Dawson, Michael Puntis, Jan Poloniecki, RM Grounds, Andrew Rhodes

Bibliographic record

VenueCritical Care · 2007
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsCritically illMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

Pulmonary intermittent thermodilution (from the pulmonary artery catheter), transpulmonary thermodilution (PiCCOplus; Pulsion, Munich, Germany) and transpulmonary lithium dilution (LiDCO™plus; LiDCO, Cambridge, UK) are all well-validated techniques in common use in intensive care for cardiac output estimation. The precision has been looked into previously and strategies to improve it have been made (that is, averaging three or four measurements over the respiratory cycle) yet not much is known about the precision of transpulmonary techniques in terms of repeatability. This study aims to look into the coefficient of variation (CV) of the lithium dilution technique in a mixed (medical/surgical) intensive care population and propose a method to improve its precision.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.027
GPT teacher head0.320
Teacher spread0.293 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueCritical CareSame topicHemodynamic Monitoring and TherapyFrench-language works237,207