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Record W2504190873 · doi:10.17925/usor.2011.04.01.38

Toric Intraocular Lenses in Cataract Surgery

2011· article· en· W2504190873 on OpenAlexaff
Rosa Braga Mele

Bibliographic record

VenuetouchREVIEWS in Ophthalmology · 2011
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsMuscular Dystrophy CanadaUniversity of Toronto
Fundersnot available
KeywordsAphakiaMedicineAstigmatismDioptreIntraocular lensOphthalmologyCataract surgeryOptometryIntraocular lensesPseudophakiaCorneal topographyCorneaVisual acuityOptics

Abstract

fetched live from OpenAlex

Cylindrical deficits in patients with corneal astigmatism of 0.50 diopter (D) to 1.00D may influence visual acuity. Increasing age and cataract surgery are correlated with greater prevalence and extent of corneal astigmatism. Conventionally, spectacles and contact lenses have been used to improve or correct corneal astigmatism. However, increasing demand for freedom from spectacles for distance vision and high prevalence of pre-existing corneal astigmatism in cataract patients have forced cataract surgery for the correction of aphakia and pre-existing corneal astigmatism to become common practice. However, implantation of toric intraocular contact lenses (IOLs) into the eye during cataract surgery may be a more predictable, powerful, and stable way of correcting pre-operative corneal astigmatism and may provide an adjunct or alternative to spectacles or relaxing incisions. Early toric IOLs were associated with post-operative rotational stability, lens misalignment, and safety concerns. The use of the new AcrySof® IQ Toric IOL for the correction of aphakia and pre-existing corneal astigmatism has largely mitigated these concerns. In addition, the AcrySof® IQ Toric IOL may also replace other treatment options for correcting pre-existing corneal astigmatism in patients undergoing cataract surgery.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.175
GPT teacher head0.378
Teacher spread0.203 · 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.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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