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Record W2895884845 · doi:10.3389/fped.2018.00290

Reducing Brain Injury of Preterm Infants in the Delivery Room

2018· review· en· W2895884845 on OpenAlexafffund
Francesca Viaroli, Po‐Yin Cheung, Megan O’Reilly, Graeme R. Polglase, Gerhard Pichler, Georg M. Schmölzer

Bibliographic record

VenueFrontiers in Pediatrics · 2018
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of AlbertaRoyal Alexandra Hospital
FundersUniversity of AlbertaWomen and Children's Health Research InstituteNational Health and Medical Research CouncilChildren's Health Research InstituteHeart and Stroke Foundation of Canada
KeywordsMedicineAnesthesiaPulse oximetryHypothermiaTraumatic brain injuryResuscitationSpinal cord injuryIntensive care medicineSpinal cord

Abstract

fetched live from OpenAlex

Cerebrovascular injury is one of the major detrimental consequences of preterm birth. Recent studies have focused their attention on factors that contribute to the development of brain lesions immediately after birth. Among those factors, hypothermia and lower cerebral oxygen saturation during delivery room resuscitation and high tidal volumes delivered during respiratory support are associated with increased risk of severe neurologic injury. In preterm infants, knowledge about causes and prevention of brain injury must be applied before and at birth. Preventive and therapeutic approaches, including correct timing of cord clamping, monitoring of physiological changes during delivery room resuscitation using pulse oximetry, respiratory function monitoring, near infrared spectroscopy, and alpha EEG, may minimize brain injury, Furthermore, postnatal administration of caffeine or other potential novel treatments (e.g., proangiogenic therapies, antioxidants, hormones, or stem cells) might improve long-term neurodevelopmental outcomes in preterm infants.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.060
GPT teacher head0.399
Teacher spread0.339 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations14
Published2018
Admission routes2
Has abstractyes

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