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Record W2980505794 · doi:10.1111/apa.15069

Survey shows marked variations in approaches to redirection of care for critically ill very preterm infants in 11 countries

2019· article· en· W2980505794 on OpenAlexafffund
Kjell Helenius, Naho Morisaki, Satoshi Kusuda, Prakesh S. Shah, Mikael Norman, Liisa Lehtonen, Brian Reichman, Brian A. Darlow, Akihiko Noguchi, Mark Adams, Dirk Bassler, Stellan Håkansson, Tetsuya Isayama, Elettra Berti, Shoo K. Lee, Máximo Vento, Kei Lui

Bibliographic record

VenueActa Paediatrica · 2019
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsPublic Health OntarioUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health Research
KeywordsCritically illMedicineIntensive careIncidence (geometry)PediatricsIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

Abstract Aim We surveyed care practices for critically ill very preterm infants admitted to neonatal intensive care units (NICUs) in the International Network for Evaluating Outcomes in Neonates (iNeo) to identify differences relevant to outcome comparisons. Methods We conducted an online survey on care practices for critically ill very preterm infants and infants with severe intracranial haemorrhage (ICH). The survey was distributed in 2015 to representatives of 390 NICUs in 11 countries. Survey replies were compared with network incidence of death and severe ICH for infants born between 23 0/7 and 28 6/7 weeks of gestation from January 1, 2015, to December 31, 2015. Results Most units in Israel, Japan and Tuscany, Italy, favoured withholding care when care was considered futile, whereas most units in other networks favoured redirection of care. For infants with bilateral grade 4 ICH, redirection of care was very frequently (≥90% of cases) offered in the majority of units in Australia and New Zealand and Switzerland, but rarely in other networks. Networks where redirection of care was frequently offered for severe ICH had lower rates of survivors with severe ICH. Conclusion We identified marked inter‐network differences in care approaches that need to be considered when comparing outcomes.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.011
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.071
GPT teacher head0.337
Teacher spread0.267 · 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

Citations12
Published2019
Admission routes2
Has abstractyes

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