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Record W2943063916 · doi:10.1097/ico.0000000000001970

Central Corneal Thickness in Childhood Cataract

2019· article· en· W2943063916 on OpenAlexaffabout
Avrey Thau, Oseluese Dawodu, Kamiar Mireskandari, Asim Ali, Nasrin Tehrani, Caroline N. DeBenedictis, Devang L. Bhoiwala, William Aultman, Waleed Abed Alnabi, Benjamin E. Leiby, Alex V. Levin

Bibliographic record

VenueCornea · 2019
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsOphthalmologyMedicineOptometry

Abstract

fetched live from OpenAlex

PURPOSE: We explored elevated central corneal thickness (CCT) in children with cataracts as possibly reflecting preexisting corneal malformation related to specific cataract morphology. METHODS: All children consecutively seen during the study periods who had cataracts and corneal pachymetry as part of their routine care were enrolled at academic centers in large cities of Canada and the United States. Study data collected included age, sex, CCT, and cataract morphology. Differences among cataract morphology groups with respect to mean CCT measurements were evaluated and compared with a historical control thickness of 558 μm. RESULTS: A total of 96 children were enrolled in this study. The average subject age was 5.1 years, and 55 children (57%) were female. The mean CCT value for all subjects was 566.1 μm. There was little evidence to conclude that the cataract morphology groups differed from each other (P = 0.65) or from controls with respect to CCT. CONCLUSIONS: In children, CCT is likely independent of cataract morphology. This implies that factors other than preoperative malformation are more likely related to elevated CCT observed in children with aphakia and pseudophakia.

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 score0.007
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.020
GPT teacher head0.309
Teacher spread0.289 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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