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Record W4391445139 · doi:10.1542/neo.25-2-e71

Potential Neurologic Manifestations of COVID-19 Infection in Neonates

2024· article· en· W4391445139 on OpenAlex
Deepika Rustogi, Garima Saxena, Saurabh Chopra, Amuchou Soraisham

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

VenueNeoReviews · 2024
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsFoothills Medical CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineCoronavirus disease 2019 (COVID-19)DiseasePediatricsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intensive care medicineNeurologic disease2019-20 coronavirus outbreakCoronavirusInfectious disease (medical specialty)PathologyOutbreak

Abstract

fetched live from OpenAlex

In contrast to adults, neonates and infants with coronavirus disease 2019 (COVID-19) infection have milder symptoms and are less likely to require hospitalization. However, some neonates with COVID-19 can present with significant symptoms. Recent evidence suggests that neurologic manifestations of neonatal COVID-19 infection may be higher than initially thought. In this comprehensive review of the current literature, we summarize the clinical, laboratory, and radiologic findings, as well as potential management strategies for COVID-19-related neurologic illness in neonates. Although the growing brain may be affected by neurologic disease associated with COVID-19 infection, the few published studies on the long-term outcomes after COVID-19 infection in neonates and infants provide conflicting results. Larger collaborative clinical studies are needed to determine whether COVID-19 infection in neonates has long-term neurodevelopmental outcomes.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.351
Teacher spread0.327 · 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