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Record W2421568985

Predicting episodes of hypotension by continuous blood volume monitoring among critically ill patients in acute renal failure on intermittent hemodialysis.

2007· article· en· W2421568985 on OpenAlexaff
Teddie Tanguay, Louise Jensen, Curt Johnston

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsRoyal Alexandra Hospital
Fundersnot available
KeywordsMedicineHemodialysisCritically illBlood pressureBlood volumeProspective cohort studyIncidence (geometry)Intravascular volume statusAnesthesiaMean arterial pressureIntensive care medicineAcute kidney injuryInternal medicineCardiologyHeart rate
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Acute renal failure (ARF) develops in 23% of all critically ill patients. Hypotension occurs in 25% to 50% of patients during intermittent hemodialysis (IHD) for ARF. Blood volume (BV) monitoring has been used in chronic renal failure, with limited use in ARF during IHD. Continuous BV monitoring in the stable critically ill patient with ARF could predict, and possibly prevent, development of hypotensive episodes. METHODS: This prospective observational study examined the relationship of BV and BV slopes to hypotension in 11 critically ill adults with ARF over three consecutive IHD Runs. The hypothesis was that there is a patient-specific critical BV and/or a specific BV slope that indicates forthcoming hypotension. RESULTS: The incidence of hypotension, according to mean arterial pressure < 70 mmHg, was 70%. No relationship was found between BV and blood pressure, and occurrence of hypotension in critically ill patients with ARF on IHD. CONCLUSION: Monitoring BV was not shown to predict hypotension in this cohort dialyzed via central venous catheters.

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)0.001
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 score0.141
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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
Published2007
Admission routes1
Has abstractyes

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