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Record W2323735264 · doi:10.1055/s-0032-1329685

Impact of Late Preterm Birth on Neonatal Intensive Care Resources in a Tertiary Perinatal Center

2012· article· en· W2323735264 on OpenAlexaff
Elliot Lyons, Prakesh S. Shah, Vibhuti Shah, Ann L Jefferies

Bibliographic record

VenueAmerican Journal of Perinatology · 2012
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineNeonatal intensive care unitGestational agePediatricsRespiratory distressBirth weightObstetricsIntensive carePregnancyAnesthesiaIntensive care medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine delivery indications, short-term morbidities, and use of resources for late preterm infants admitted to the neonatal intensive care unit (NICU) at a tertiary perinatal center. STUDY DESIGN: Data for 1137 inborn infants 340/7 to 366/7 weeks' gestational age discharged between July 2004 and December 2009 were collected from an electronic NICU database. Birth information was obtained from maternal charts. RESULTS: Forty-two percent of late preterm infants were admitted to the NICU. Their mean ( ± standard deviation) birth weight was 2347 ± 569 g; 15.1% were small for gestational age, 35.5% were multiples, and 17.8% had an antenatally diagnosed anomaly. Most births (52%) occurred following spontaneous rupture of membranes or labor. Cesarean section rate was 56.8%. Mortality rate was 1.2%. Most frequent morbidities were transient tachypnea (18.8%), cardiac or other congenital anomaly (16.8%), and respiratory distress syndrome (7.4%). Although 41.5% received ventilatory support, duration was short (1.1 ± 3.1 days). Mean length of NICU stay was 8.1 ± 9.3 days with 38% transferred to community hospitals before discharge. CONCLUSION: For many late preterm infants admitted to the NICU, the duration of intensive therapy was short and some required no interventions. One impact of late preterm birth was bed occupancy.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.617

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 head0.019
GPT teacher head0.369
Teacher spread0.351 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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