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Record W4403682813 · doi:10.1093/pch/pxae067.071

72 Hospital level of service, rural-urban hospital location, and neonatal resuscitation interventions at birth: A population-based study in Alberta, Canada from 2000-2020

2024· article· en· W4403682813 on OpenAlexaboutno aff
Breanna Pickett, S Dg I M Crawford, Deborah McNeil, Georg M. Schmölzer, Amuchou Soraisham, Bo Pan, Heather Shonoski, Khalid Aziz, Brenda Hiu Yan Law

Bibliographic record

VenuePaediatrics & Child Health · 2024
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMedicineNeonatal resuscitationResuscitationEmergency medicineMedical emergencyPopulationEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Abstract Background Advanced neonatal resuscitation interventions (ANRIs: endotracheal intubation, chest compressions, and epinephrine) are rarely needed for infants born at ≥34 weeks gestational age (GA). However, healthcare providers (HCPs) in community hospitals can encounter the need for ANRIs, while having less experience and resources than HCPs in centers with neonatal intensive care units (NICUs). Understanding practice differences between hospitals of different levels of service (LofS) and rural/urban location can inform quality improvement to reduce health disparities. Objectives To examine how hospital level of service and rural/urban location relate to ANRI rates in Alberta, Canada, a universal public health system with standardized Neonatal Resuscitation Program (NRP) training. Design/Methods All live births ≥34 weeks GA in Alberta from 2000-2020 inclusive were examined using retrospective, population-level administrative data from the Alberta Perinatal Health Program (APHP). Hospitals (n=97) were contacted to determine annual LofS during the study period, with levels (0,1A/B/C,2,3) based on provincial obstetrical triage for availability of delivery support, cesarean-sections, obstetricians, paediatricians/neonatologists, and NICUs. Hospitals were further subcategorized by population and proximity to metropolitan centers.(Table 1) Rates of individual interventions or any ANRI were compared. Results There were 971,403 live births during the study period with a mean GA of 38.9 weeks; 1.6% were missing ANRI data, 955,835 births were included. Most occurred in hospitals with level 2 (57.4%) or level 3 (21.8%) NICUs; few occurred at sites with no delivery support (Level 0, 0.1%). ANRI rates were intubation (0.8%), chest compression (0.2%), and epinephrine (0.02%), any ANRI (0.96%, ~1/1000 live births). However, chest compression rates progressively decrease, and intubation rates progressively increase as LofS increases.(Table 1, Figure 1) When considering rural/urban location, there were outliers in chest compression rates in remote 0 (25.4/1000 live births) and metro-influenced 1A (30.2/1000 live births), intubation in remote 1C (33.3/1000 live births) and metro 0 (193.5/1000 live births) and epinephrine in metro 0 (64.5/1000 live births). Conclusion In this population-based cohort study of ≥34 weeks births, there were higher chest compressions rates and lower intubations rates at hospitals without NICUs despite standardized training. Reasons for this difference require further investigation; potential solutions such as targeted outreach education and introduction of laryngeal mask airways could be explored.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.028
GPT teacher head0.328
Teacher spread0.300 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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