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Record W2810334983 · doi:10.1097/ico.0000000000001683

Endothelial Cell Loss in Obstetric Forceps-Related Corneal Injury: A Longitudinal Specular Microscopic Study

2018· article· en· W2810334983 on OpenAlexaffabout
Ali El Hamouly, S. Fung, Hamza Sami, Dishay Jiandani, Sara Williams, Kamiar Mireskandari, Asim Ali

Bibliographic record

VenueCornea · 2018
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineForcepsCorneal endotheliumOphthalmologyCorneaVisual acuitySignificant differenceSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To assess the impact of ocular trauma associated with obstetric forceps delivery on the corneal endothelium in children. METHODS: Five patients who attended the Hospital for Sick Children, Toronto, between 2013 and 2016 with a diagnosis of unilateral obstetrical forceps-related corneal injury were included. Clinical presentation, best-corrected visual acuity, corneal endothelial cell density (ECD, cells/mm), and measures of cellular morphology were obtained. RESULTS: The mean follow-up duration was 30 ± 10 months. Mean ECD in the affected eye at initial assessment was significantly lower than that of the fellow eye (2576 ± 733 vs. 3481 ± 288 cells/mm, P = 0.02). At final follow-up, mean ECD was 3293 ± 175 and 1907 ± 524 cells/mm in the normal and affected eyes, respectively (P = 0.004). The mean annual rate of the ECD decrease was higher in the affected eyes than in the normal eyes (9.1% ± 4.2% vs. 2.0% ± 2.5%), although this difference was not statistically significant (P = 0.06). CONCLUSIONS: In children with forceps-related corneal injury, lower ECD with a higher annual decrease can be assessed with specular microscopy for risk stratification and parental counseling purposes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.279
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2018
Admission routes2
Has abstractyes

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