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Record W2973244446 · doi:10.1111/apa.15021

Early fluid overload was associated with prolonged mechanical ventilation and more aggressive parameters in critically ill paediatric patients

2019· article· en· W2973244446 on OpenAlexaff
Clarice Laroque Sinott Lopes, Guilherme Unchalo Eckert, Taís Sica da Rocha, Patrícia S. Fontela, Jefferson Pedro Piva

Bibliographic record

VenueActa Paediatrica · 2019
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineInterquartile rangeMechanical ventilationRenal replacement therapyIntensive care unitRetrospective cohort studyCritically illPopulationAnesthesiaPediatricsInternal medicine

Abstract

fetched live from OpenAlex

AIM: We evaluated the influence of early fluid overload on critically ill children admitted to a paediatric intensive care unit by examining mechanical ventilation (MV), mortality, length of stay and renal replacement therapy. METHODS: This retrospective cohort study covered January 2015 to December 2016 and focused on all episodes of MV support that exceeded 24 hours. The fluid overload percentage (FO%) was calculated daily for the first 72 hours and we estimated its effect on outcomes. RESULTS: We included 186 MV episodes in 154 patients. The median age was 13.8 months, with an interquartile range (IQR) of 3.8-34.0 months, and the mortality rate was 12.4%. The median FO% in the first 72 hours was 8.0% (IQR 3.6%-11.2%). An FO% of ≥10% was associated with higher ventilatory parameters, namely peak inspiratory pressure (P = .023) and positive end expiratory pressure (P = .003), and renal replacement therapy (P = .02) and higher mortality (8.8% vs 19.7%). In a multivariate Cox regression model, FO ≥ 10% at 72 hours was independently associated with longer MV support, but not mortality (P = .001). CONCLUSION: In a heterogeneous paediatric population given MV, an early cumulative FO of ≥10% was associated with more aggressive ventilatory parameters and prolonged length of MV, but not mortality.

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)0.000
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 score0.006
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.260
Teacher spread0.243 · 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
Published2019
Admission routes1
Has abstractyes

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