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Record W2592954932 · doi:10.21037/atm.2017.01.68

Diaphragmatic ultrasound as a monitoring tool in the intensive care unit

2017· letter· en· W2592954932 on OpenAlexaff
Ioanna Sigala, Theodoros Vassilakopoulos

Bibliographic record

VenueAnnals of Translational Medicine · 2017
Typeletter
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineDiaphragmatic breathingIntensive care unitIntensive care medicineCritically illDiaphragm (acoustics)Mechanical ventilationRespiratory monitoringIntensive careOxygenationVentilation (architecture)Respiratory systemInternal medicine

Abstract

fetched live from OpenAlex

Critically ill patients are a group of patients with special needs during hospitalization. The vast majority of them is mechanically ventilated and requires continuous assessment of vital parameters. Intensive care unit (ICU) is the environment that renders this monitoring feasible. A lot of parameters characterizing cardiac function, hemodynamic status, pulmonary function—oxygenation, acid base balance, renal function, nutrition and gastrointestinal function are continuously or repetitively monitored. It is quite impressive that assessment of respiratory muscles, and specifically of the diaphragm, is lacking in the daily practice of ICUs. This is the case despite the fact that there is robust evidence about diaphragmatic dysfunction in ICU patients under mechanical ventilation.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.394
Threshold uncertainty score0.909

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 integrity0.0000.001
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 head0.143
GPT teacher head0.395
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations13
Published2017
Admission routes1
Has abstractyes

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