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Thirty-three–gauge hypodermic needle–guided iris suturing technique

2020· article· en· W3112432781 on OpenAlexaff
Steven G. Safran, Sarah G. Bonaffini, Usma Chatha

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsHypodermic needleSewing machineIRIS (biosensor)Fibrous jointMedicineSurgeryProleneComputer scienceArtificial intelligenceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The purpose of this study was to describe a new 33-gauge hypodermic needle-guided iris suturing technique for repair of iris defects and its adaptation for use to address a wide range of iris pathology in complex anterior segment reconstruction cases. This approach uses a 33-gauge hypodermic needle loaded with a 10-0 Prolene suture within its lumen, which is then used to directly introduce and manipulate the suture within the anterior chamber. Eliminating the need for a needle holder, this technique provides surgeons improved ergonomics and minimizes risk for trauma to adjacent tissues. Improving on and addressing limitations of the original sewing machine technique described by Safran in 1995, iris repair using this technique has been applied successfully in 15 patients to achieve satisfactory visual and cosmetic outcomes without complications. Multiple iris suturing strategies can be combined on a given case to attain complex repair with this new approach.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.048
GPT teacher head0.285
Teacher spread0.237 · 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 designBench or experimental
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

Citations3
Published2020
Admission routes1
Has abstractyes

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