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Record W2751394766 · doi:10.1111/apa.14059

Elective transfers of preterm neonates to regional centres on non‐invasive respiratory support is cost effective and increases tertiary care bed capacity

2017· article· en· W2751394766 on OpenAlexaffabout
Hussein Zein, Kamran Yusuf, Renee Paul, Derek Kowal, Sumesh Thomas

Bibliographic record

VenueActa Paediatrica · 2017
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineGestational ageIntensive careContinuous positive airway pressureBirth weightPediatricsLow birth weightEmergency medicineTertiary careIntensive care medicineAnesthesiaPregnancy

Abstract

fetched live from OpenAlex

AIM: Managing capacity at regional facilities caring for sick neonates is increasingly challenging. This study estimated the clinical and economic impact of the elective transfer of stable infants requiring nasal continuous positive airway pressure (NCPAP) from level three to level two neonatal intensive care units (NICUs) within an established clinical network of five NICUs. METHODS: We retrospectively analysed the records of 99 stable infants transferred on NCPAP between two level three NICUs and three level two NICUs in Calgary, Canada, between June 2014 and May 2016. RESULTS: The median gestational age and weight at birth were 28 weeks and 955 g, and the median corrected gestational age and weight at transfer were 33 weeks and 1597 g, respectively. This resulted in cost savings of $2.65 million Canadian dollars during the two-year study period, and 848 level three NICU days were freed up for potentially sick neonates. There were no adverse events associated with the transfers. CONCLUSION: The elective transfer of stable neonates on NCPAP from level three to level two NICUs within an established clinical network led to substantial cost savings, was safe and increased the bed capacity at the two level three NICUs.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.029
GPT teacher head0.331
Teacher spread0.302 · 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.

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

Citations8
Published2017
Admission routes2
Has abstractyes

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