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Record W2922868682 · doi:10.1159/000497163

Optic Nerve Sheath Diameter for Preterm Infants: A Pilot Study

2019· article· en· W2922868682 on OpenAlexaff
Sarah Ardell, Sibasis Daspal, Tanya Holt, Gregory Hansen

Bibliographic record

VenueNeonatology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineOptic nerveAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: In preterm infants, early diagnosis and management of a raised intracranial pressure (ICP) may be important to improve neurodevelopmental outcomes. While invasive ICP monitoring is not recommended, ultrasonography of the optic nerve sheath diameter (ONSD) could provide a noninvasive alternative to evaluate ICP. The objective of this pilot study was to document ranges of ONSD in preterm infants. METHODS: This prospective cohort pilot evaluated preterm infants who were admitted to the neonatal intensive care unit without suspected raised ICP. Three images per eye were obtained from a 20-5 MHz linear array ultrasound transducer placed on the patient's superior eyelid. The OSND was measured 3 mm behind the globe. A second ultrasonographer duplicated half of the scans. Multiple linear regression analysis was conducted for both right and left ONSD with corrected gestational age, weight, and head circumference as predictors. Lin's concordance assessed interrater reliability. RESULTS: In 12 preterm infants 114 scans were performed on both eyes. The median age was 33 weeks (corrected gestational age) with a range of 29-36 weeks. Corrected gestational age was the strongest predictor for ONSD, and preliminary measurements at each gestational age were established. Interrater reliability demonstrated substantial agreement (Qc = 0.97). CONCLUSION: In preterm infants, ONSD strongly correlates with corrected gestational age. These data should be validated with other imaging modalities before abnormal ranges can be considered.

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 score0.183
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.287
Teacher spread0.264 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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