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Record W4210612732 · doi:10.3390/biomedicines10020347

Pathophysiology of Perinatal Asphyxia in Humans and Animal Models

2022· review· en· W4210612732 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomedicines · 2022
Typereview
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePerinatal asphyxiaEncephalopathyHypothermiaAsphyxiaNeuroprotectionHypoxic Ischemic EncephalopathyHypoxia (environmental)PathophysiologyIntensive care medicineErythropoietinAnesthesiaPathologyInternal medicine

Abstract

fetched live from OpenAlex

Perinatal asphyxia is caused by lack of oxygen delivery (hypoxia) to end organs due to an hypoxemic or ischemic insult occurring in temporal proximity to labor (peripartum) or delivery (intrapartum). Hypoxic-ischemic encephalopathy is the clinical manifestation of hypoxic injury to the brain and is usually graded as mild, moderate, or severe. The search for useful biomarkers to precisely predict the severity of lesions in perinatal asphyxia and hypoxic-ischemic encephalopathy (HIE) is a field of increasing interest. As pathophysiology is not fully comprehended, the gold standard for treatment remains an active area of research. Hypothermia has proven to be an effective neuroprotective strategy and has been implemented in clinical routine. Current studies are exploring various add-on therapies, including erythropoietin, xenon, topiramate, melatonin, and stem cells. This review aims to perform an updated integration of the pathophysiological processes after perinatal asphyxia in humans and animal models to allow us to answer some questions and provide an interim update on progress in this field.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.064
GPT teacher head0.335
Teacher spread0.271 · 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