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Record W1976424638 · doi:10.1055/s-0030-1253405

Impact of Target Blood Gases on Outcome in Congenital Diaphragmatic Hernia (CDH)

2010· article· en· W1976424638 on OpenAlexaff
Mary Brindle, Irene Ma, Erik D. Skarsgard

Bibliographic record

VenueEuropean Journal of Pediatric Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicCongenital Diaphragmatic Hernia Studies
Canadian institutionsUniversity of British ColumbiaSt. Mary's UniversityUniversity of Calgary
FundersUniversity of Pittsburgh
KeywordsMedicineCongenital diaphragmatic herniaDiaphragmatic breathingOutcome (game theory)HerniaDiaphragmatic herniaSurgeryPathologyPregnancyFetus

Abstract

fetched live from OpenAlex

INTRODUCTION: Neonatal intensive care unit (NICU) stabilization strategies which normalize physiology according to predetermined blood gas targets may contribute to observed improved survival rates of patients with CDH. The purpose of our study was to compare risk-adjusted outcomes of CDH patients managed with or without blood gas targets established at NICU admission. METHODS: Cases were collected from a national CDH network between May 2005 and November 2007. On NICU admission, the responsible neonatologist was asked to establish target ranges for pH, pCO (2), pO (2), and pre/post-ductal O (2) saturation. The outcomes analyzed were mortality, need for ECMO, days of mechanical ventilation/supplemental oxygen, and length of stay. RESULTS: Of 147 CDH infants, 63 had admission blood gas targets. Severity of illness and gestational age in both groups were comparable (SNAP-II score). Infants with blood gas targets had a significantly lower mortality than those without (Hazard ratio 0.27, p=0.006). CONCLUSIONS: Blood gas targets for the management of infants with CDH are associated with improved survival. Although the willingness to create and use stabilization targets to guide early NICU care may be a surrogate for other factors (experience, staffing, lack of interest), it is clearly associated with improved survival in CDH.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.008
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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