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Record W4387120794 · doi:10.3390/life13101979

Optic Nerve Sheath Diameter: A Cross-Sectional Study of Ultrasonographic Measurement in Healthy Black South African Adults

2023· article· en· W4387120794 on OpenAlexaboutno aff
Lindy Trollip, Kerry Alberto, Aubrey Makgotloe

Bibliographic record

VenueLife · 2023
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOptic nerveNerve sheathUltrasoundIntracranial pressurePopulationOphthalmologyNuclear medicineSurgeryRadiology

Abstract

fetched live from OpenAlex

Ultrasonographic optic nerve sheath diameter (ONSD) measurement is an accurate, portable, and non-invasive method of detecting raised intracranial pressure that can also reflect dynamic, real-time changes in intracranial pressure fluctuations. Various studies have shown the mean range of ONSD to vary greatly across different population groups. This study aimed to determine the mean ONSD in healthy Black South African adults. In this cross-sectional study, healthy black South African adult participants underwent optic nerve sheath ultrasound of the right eye, with the diameter being measured at 3 mm behind the retina in two different planes. The average of the two measurements was used to find the mean optic nerve sheath diameter. This measurement was compared to that found in a Canadian adult population, and the effect of age, gender, and co-morbidities on ONSD was assessed. A total of 99 participants were included in this study, of which 39 were male and 60 were female. The mean ONSD was found to be 5.1 mm (SD ± 0.33). This value was significantly higher than the mean ONSD observed in the Canadian population (p < 0.001). There was no significant difference found between the mean ONSD in males and females (p = 0.652), and both age and presence of co-morbidities were not found to significantly correlate with ONSD. (p = 0.693 and p = 0.974, respectively).

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)0.001
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 score0.004
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.070
GPT teacher head0.305
Teacher spread0.235 · 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
Published2023
Admission routes1
Has abstractyes

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