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Record W2313810121 · doi:10.3928/15428877-20120927-02

Intraocular Lens Position Following In-the-Bag Implantation of Single-Piece Versus Three-Piece Acrylic Intraocular Lenses

2012· article· en· W2313810121 on OpenAlexaffabout
Marie-Claude Robert, Paul Harasymowycz

Bibliographic record

VenueOphthalmic surgery, lasers & imaging retina · 2012
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsScheimpflug principleIntraocular lensMedicineIntraocular lensesOphthalmologyIRIS (biosensor)CapsulorhexisPhacoemulsificationOptometryVisual acuityCornea

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: To report the position of the single-piece and three-piece intraocular lenses (IOLs) following in-the-bag implantation. PATIENTS AND METHODS: Forty patients with bilateral age-related cataracts were recruited from both tertiary hospital and private practice in Montréal, Canada. Patients received the single-piece IOL in one eye and the three-piece IOL contralaterally. Postoperative anterior chamber depth (ACD) and iris-to-IOL distance were evaluated using a Scheimpflug imaging system. RESULTS: Mean ACD was 4.21 ± 0.32 mm for the single-piece IOL and 3.94 ± 0.34 mm for the three-piece IOL. Mean iris-to-IOL distance was 0.70 ± 0.19 mm for the single-piece IOL and 0.44 ± 0.21 mm for the three-piece IOL. The difference between paired eyes was 0.26 ± 0.20 mm (P = .002) for ACD and 0.25 ± 0.21 mm (P < .001) for iris-to-IOL distance. CONCLUSION: The single-piece IOL was positioned more posteriorly to the iris and allowed for a greater ACD than the three-piece IOL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0020.000

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 head0.034
GPT teacher head0.279
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2012
Admission routes2
Has abstractyes

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