MétaCan
Menu
Back to cohort
Record W1983788132 · doi:10.1097/ico.0b013e318199fa2c

IntraLase-Enabled Astigmatic Keratotomy for Correction of Astigmatism After Descemet Stripping Automated Endothelial Keratoplasty: A Case Report

2009· article· en· W1983788132 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCornea · 2009
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAstigmatismMedicineOphthalmologyVisual acuityOptometryOptics

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to report on the treatment of high astigmatism after Descemet stripping automated endothelial keratoplasty with IntraLase (IntraLase, Inc., Irvine, CA) -enabled astigmatic keratotomy (IEAK). METHODS: A 85-year-old patient with pseudophakic bullous keratopathy underwent an intraocular lens exchange and Descemet stripping automated endothelial keratoplasty surgery on his left eye. Four months after surgery, high astigmatism was treated with IEAK. RESULTS: Preoperative uncorrected visual acuity was 20/300 and best spectacle-corrected visual acuity was 20/100 with a refraction of -4.00 + 5.75 x 150. Seven months post-IEAK, uncorrected visual acuity was improved to 20/60 and best spectacle-corrected visual acuity 20/40-2 with a refraction of -1.50 + 2.75 x 100. CONCLUSION: IEAK, as presented here, can be used for the correction of high post-Descemet stripping automated endothelial keratoplasty astigmatism.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.270
Teacher spread0.256 · 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