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Record W2005142659 · doi:10.1016/j.jcrs.2014.02.042

Ocular anterior segment changes in pregnancy

2014· article· en· W2005142659 on OpenAlexaff
Yakov Goldich, Michael Cooper, Yaniv Barkana, Josef Tovbin, Karin Lee Ovadia, Isaac Avni, David Zadok

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsBarrick Gold (Canada)
Fundersnot available
KeywordsAnterior Eye SegmentPregnancyOphthalmologyMedicineOptometryCorneaBiology

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate the changes occurring in the cornea, anterior segment anatomy, and intraocular pressure (IOP) in pregnant women. SETTING: Department of Ophthalmology, Assaf Harofeh Medical Center, Zerifin, Israel. DESIGN: Prospective single-center comparative study. METHODS: The Ocular Response Analyzer dynamic bidirectional applanation device and the Pentacam HR Scheimpflug imaging system were used to obtain data on the anterior eye segments of healthy pregnant and nonpregnant women. RESULTS: Sixty pregnant and 60 nonpregnant women were enrolled. The Goldmann-correlated IOP and corneal-compensated IOP were significantly lower in the pregnant group (mean 10.96 mm Hg versus 12.97 mm Hg, P<.001; and 10.97 mm Hg versus 13.16 mm Hg, P<.001, respectively). The corneal front steep keratometry value was statistically significantly higher in the pregnant group (44.81 diopters [D] versus 44.1 D, P=.039). No significant difference was found in corneal hysteresis, the corneal resistance factor, corneal posterior curvature, central corneal thickness and volume, anterior chamber depth and volume, or iridocorneal angle. CONCLUSIONS: Pregnancy was associated with greater corneal curvature and lower IOP. Further studies should be performed to learn whether these alterations result from changes in corneal biomechanical properties during pregnancy. FINANCIAL DISCLOSURE: No author has a financial or proprietary interest in any material or method mentioned.

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)0.001
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 score0.439
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.272
Teacher spread0.255 · 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

Citations53
Published2014
Admission routes1
Has abstractyes

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