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High frequency jet ventilation versus high frequency oscillatory ventilation for pulmonary dysfunction in preterm infants

2016· review· en· W2108296176 on OpenAlexaff
Yahya H Al Ethawi, Ayman Abou Mehrem, John Minski, Chelsea Ruth, Peter G. Davis

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2016
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsChildren's Hospital of WinnipegUniversity of ManitobaFoothills Medical CentreHealth Sciences CentreUniversity of CalgaryWinnipeg Regional Health Authority
Fundersnot available
KeywordsMedicineHigh-frequency ventilationRandomized controlled trialGestational ageVentilation (architecture)Bronchopulmonary dysplasiaRespiratory distressPediatricsMechanical ventilationIntensive care medicinePregnancyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Respiratory distress syndrome (RDS) is considered one of the major contributors to severe pulmonary dysfunction and consequent death in preterm infants. Despite widespread improvements in care, including increased utilization of antenatal steroids, use of surfactant replacement therapy, and advances in conventional mechanical ventilation (CMV), chronic lung disease (CLD) occurs in 42% of surviving preterm infants born at less than 28 weeks gestational age (GA). High frequency ventilation (HFV) aims to optimize lung expansion while minimizing tidal volume (Vt) to decrease lung injury. Two methods of HFV - high frequency oscillatory ventilation (HFOV) and high frequency jet ventilation (HFJV) - are widely used, but neither has demonstrated clear superiority in elective or rescue mode. OBJECTIVES: To compare the benefits and side effects of HFJV versus HFOV for mortality and morbidity in preterm infants born at less than 37 weeks GA with pulmonary dysfunction in both elective and rescue modes. SEARCH METHODS: We used the standard search strategy of the Cochrane Neonatal Review Group to search the Cochrane Central Register of Controlled Trials (CENTRAL; 2015, Issue 11), MEDLINE via PubMed (1966 to November 30, 2015), EMBASE (1980 to November 30, 2015), and the Cumulative Index to Nursing and Allied Health Literature (CINAHL) (1982 to November 30, 2015). We also searched clinical trials databases, conference proceedings, and the reference lists of retrieved articles for randomized controlled trials and quasi-randomized trials. We imposed no date, language, or publication restrictions. SELECTION CRITERIA: We planned to include randomized, cluster-randomized, and quasi-randomized controlled trials if study authors stated explicitly that groups compared in the trial were established by a random or systematic method of allocation. We planned to exclude cross-over studies, as they would not allow assessment of the outcomes of interest. DATA COLLECTION AND ANALYSIS: We used the standard methods of the Neonatal Cochrane Review Group, including independent trial assessment and data extraction. We intended to analyze the data by using risk ratios (RRs) and risk differences (RDs) and 1/RD. We planned to calculate the number needed to treat for an additional beneficial outcome (NNTB) or the number needed to treat for an additional harmful outcome (NNTH). MAIN RESULTS: We found no studies that met our inclusion criteria. AUTHORS' CONCLUSIONS: We found no evidence to support the superiority of HFJV or HFOV as elective or rescue therapy. Until such evidence is available, comparison of potential side effects or presumed benefits of either mode is not feasible.

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)0.006
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.004
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.154
GPT teacher head0.428
Teacher spread0.275 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations39
Published2016
Admission routes1
Has abstractyes

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