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Total keratometry for determination of true corneal power after myopic small-incision lenticule extraction

2021· article· en· W3133844419 on OpenAlexaff
Roman Lischke, Wolfgang J. Mayer, Nikolaus Feucht, Jakob Siedlecki, Rainer Wiltfang, Daniel Kook, Siegfried Priglinger, Nikolaus Luft

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsKeratometerDioptreOphthalmologyMedicineCorneal topographyAstigmatismProspective cohort studyCorneaSurgeryPhysicsVisual acuityOptics

Abstract

fetched live from OpenAlex

PURPOSE: To gauge the value of total keratometry (TK) to estimate corneal power change in eyes that underwent small-incision lenticule extraction (SMILE) for treatment of myopia or myopic astigmatism. SETTING: Department of Ophthalmology, Ludwig-Maximilians-University, and SMILE Eyes Clinic Munich Airport, Munich, Germany. DESIGN: Prospective cross-sectional trial. METHODS: A total of 40 eyes of 40 patients who had undergone myopic SMILE were enrolled in this prospective study. Total corneal refractive power (TCRP; Pentacam HR) and TK (IOLMaster 700, Carl Zeiss Meditec AG) values were compared with the clinical history method (CHM). The surgically induced changes in TCRP (ΔTCRP) and TK (ΔTK) were also compared with the changes in spherical equivalent on the corneal plane (ΔSEco). RESULTS: Of the 40 eyes analyzed, the correlation between TK and CHM (R2 = 0.91, P < .001) was stronger than that between TCRP and CHM (R2 = 0.87, P < .001). When compared with the CHM, TCRP underestimated corneal power by a mean relative error of 0.59 diopter (D) and TK by 0.17 D. Linear regression analysis of ΔTCRP/ΔTK and the difference between preoperative and postoperative manifest refraction spherical equivalent at the corneal plane (ΔSEco) showed stronger correlation in ΔTK (R2 = 0.88) than that in ΔTCRP (R2 = 0.82). CONCLUSIONS: The findings endorse TK as an accurate measure for corneal power after myopic SMILE.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.302
Teacher spread0.281 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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