MétaCan
Menu
Back to cohort
Record W2064286775 · doi:10.1016/j.jcrs.2003.08.008

Piggyback intraocular lens implantation to correct myopic pseudophakic refractive error after penetrating keratoplasty

2004· article· en· W2064286775 on OpenAlexaffabout
Robert A. Paul, Hall F. Chew, Neera Singal, David S. Rootman, Allan R. Slomovic

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2004
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsEmmetropiaDioptreMedicineIntraocular lensOphthalmologyRefractive errorVisual acuityOptometry

Abstract

fetched live from OpenAlex

PURPOSE: To determine the safety and efficacy of implanting a second intraocular lens (IOL) to correct myopic pseudophakic refractive error after penetrating keratoplasty (PKP). SETTING: Department of Ophthalmology, Toronto Western Hospital, Toronto, Ontario, Canada. METHODS: In this retrospective case series, 6 eyes of 6 post-PKP pseudophakic patients had a second piggyback IOL implantation to correct a residual myopic refractive error. The uncorrected visual acuity (UCVA) and the best corrected visual acuity (BCVA) were measured at regular intervals during a 7-month follow-up. Efficacy was determined by the achieved refractive correction and Snellen UCVA measurements. Safety was measured by loss of BCVA and complications (intraoperative and postoperative). RESULTS: The UCVA improved in all cases. Five patients achieved a BCVA of 20/40 or better postoperatively. Before surgery, the mean spherical equivalent (SE) was -8.08 diopters (D) (range -6.13 to -12.00 D). After surgery, the mean SE was -0.94 D (range -2.38 to +0.25 D). Four patients were within +/-1.50 D of emmetropia. There were no intraoperative or postoperative complications. CONCLUSION: Implanting a piggyback IOL was a safe and effective means of correcting myopic pseudophakic refractive error post PKP.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.345
Teacher spread0.312 · 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

Citations22
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Cataract & Refractive SurgerySame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207