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Record W1598835306 · doi:10.1111/crj.12155

Hemodynamic changes in child acute respiratory distress syndrome with airway pressure release ventilation: a case series

2014· article· en· W1598835306 on OpenAlexaff
Atsushi Kawaguchi, Gonzalo Garcia Guerra, Jonathan P. Duff, Ikuya Ueta, Ryosuke Fukushima

Bibliographic record

VenueThe Clinical Respiratory Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsStollery Children's HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineARDSAnesthesiaHemodynamicsRespiratory distressBlood pressurePopulationInternal medicineLung

Abstract

fetched live from OpenAlex

BACKGROUND: Airway pressure release ventilation (APRV) is widely used in adult critical care settings. However, information on the use of APRV in the pediatric population is limited. METHODS: All patients admitted to the medical-surgical pediatric intensive care unit with a diagnosis of acute respiratory distress syndrome (ARDS) who received APRV for at least 12 h between 2007 and 2009 were reviewed. RESULTS: Thirteen patients with a variety of etiologies of ARDS were included, with a mean weight of 18.2 ± 15.0 kg, a mean age of 68 ± 57 months and a predicted mortality (based on Pediatric Index of Mortality version 2) of 23.9 ± 13.8%. Patients were placed on APRV for a median of 4 days (range 1-10 days). There was no change in blood gas parameters after 1 h or 12 h of APRV when compared with pre-APRV. There was no statistical difference in hemodynamic parameters, including mean arterial blood pressure, central venous blood pressure and heart rate, while the patients were on APRV. CONCLUSION: APRV could be safely used in pediatric ARDS patients, without significant hemodynamic compromise or side effects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.326
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations10
Published2014
Admission routes1
Has abstractyes

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