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Record W2143121855 · doi:10.1080/02699050010007407

A computer system for continuous long-term recording, processing, and analysis of physiological data of brain injured patients in ICU settings

2001· article· en· W2143121855 on OpenAlexaff
Ivan Kropyvnytskyy, Fraser W. Saunders, Peter Schierek, Margreet Pols

Bibliographic record

VenueBrain Injury · 2001
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer scienceTerm (time)Data acquisitionMedicineSimulationReal-time computingOperating system

Abstract

fetched live from OpenAlex

The objective of this project was to assemble and test a computer-based system for continuous long-term physiological data acquisition. The system would be used to study short-term (heart rate variability) and long-term (circadian rhythms) dynamics of physiological parameters in severely brain injured patients in ICU settings. A system has been built using open-source software and the Linux operating system as the platform. The system consists of three main parts: data acquisition, processing and analysis. The system was tested in ICU and experimental settings for long periods of time (up to 10 days of non-stop recording). The system appeared to function properly and accurately. Samples of the data according to the stages of acquisition-analysis process are presented in the paper. Avenues for the system use and development are discussed.

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 score0.194
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.309
Teacher spread0.274 · 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

Citations16
Published2001
Admission routes1
Has abstractyes

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