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Record W4416224773 · doi:10.1186/s12887-025-06306-z

Narrative review of neurodevelopmental and psychiatric complications associated with prematurity

2025· article· en· W4416224773 on OpenAlexafffund
Halimat Ibrahim, Sumera Aziz Ali, Sarka Lisonkova, Natalie Chan, Joseph Ting

Bibliographic record

VenueBMC Pediatrics · 2025
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsWomen's Health Research InstituteUniversity of British ColumbiaUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta InnovatesUniversity of Alberta
KeywordsNarrative reviewAffect (linguistics)MEDLINEBronchopulmonary dysplasiaIntensive carePremature birthReview article

Abstract

fetched live from OpenAlex

Prematurity, defined as birth before 37 weeks of gestation, remains a significant global health concern due to its strong association with increased infant morbidity and mortality. Despite significant advances in antenatal care and neonatal intensive care, preterm infants remain highly susceptible to complications including bronchopulmonary dysplasia, intraventricular hemorrhage, and necrotizing enterocolitis. These conditions not only affect immediate survival but also contribute to long-term neurodevelopmental and neuropsychiatric challenges that may persist throughout the life span. Survivors of preterm born infants continue to face higher risks of cognitive, motor, and behavioural impairments, as well as psychiatric disorders such as attention-deficit/hyperactivity disorder, anxiety, and depression. This narrative review synthesises the recent findings regarding long-term impacts of prematurity on neurodevelopmental and neuropsychiatric outcomes. It highlights their incidence, risk factors, and the screening and assessment tools currently used in clinical and research settings. By synthesising current knowledge, the review aims to guide clinical care, support early identification of at-risk infants, and inform future research priorities.

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)0.000
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 score0.033
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.281
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

Citations3
Published2025
Admission routes2
Has abstractyes

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