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Record W1981953215 · doi:10.1055/s-0033-1347364

Impact of Late Preterm and Early Term Infants on Canadian Neonatal Intensive Care Units

2013· article· en· W1981953215 on OpenAlexaffabout
Prakesh S. Shah, Vibhuti Shah, Xiang Y. Ye, Shoo Lee, Ann L Jefferies, Kate Bassil

Bibliographic record

VenueAmerican Journal of Perinatology · 2013
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsPublic Health OntarioUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineIntensive careTerm (time)Neonatal intensive care unitPediatricsIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the short-term morbidities, mortality, and use of neonatal intensive care unit (NICU) resources for late preterm, early term, and term infants. STUDY DESIGN: Infants born between 34 and 40 weeks of gestation and admitted to a Canadian NICU in 2010 were designated late preterm (340/7 to 366/7 weeks), early term (370/7 to 386/7 weeks), or term (390/7 to 406/7 weeks). Mortality, short-term morbidities, and resource utilization were compared between the three groups using chi-square tests and analysis of variance. RESULTS: Among 6,636 included infants, 44.2% (n = 2,935) were late preterm, 26.2% (n = 1,737) early term, and 29.6% (n = 1,964) term. Term infants were more likely to require resuscitation at birth and had lower Apgar scores than late preterm and early term infants (p < 0.001). Length of stay and need for respiratory support decreased with increasing gestational age; however, the proportion of hospital days that intensive care was required increased. CONCLUSION: The greatest impact of late preterm infants is on NICU bed occupancy, whereas for term infants it is on intensity of care. Early term infants experience greater rates of some complications than term, demonstrating that risk persists for these infants. These findings have important implications for NICU resource planning and practice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.354
Teacher spread0.332 · 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

Citations17
Published2013
Admission routes2
Has abstractyes

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