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Record W2738118591 · doi:10.1097/ico.0000000000001288

Simple Preoperative Ink Test as a Novel Adjunct to Intrastromal Keratopigmentation for Post-laser Peripheral Iridotomy Dysphotopsias

2017· article· en· W2738118591 on OpenAlexaff
Stephan Ong Tone, Daniel Q. Li, Zach Ashkenazy, Armand Borovik, Allan R. Slomovic, David S. Rootman, Clara C. Chan

Bibliographic record

VenueCornea · 2017
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineOphthalmologyPeripheralIRIS (biosensor)AdjunctSurgeryInternal medicineComputer science

Abstract

fetched live from OpenAlex

PURPOSE: To describe a simple preoperative ink test as a novel adjunct to intrastromal keratopigmentation for post-laser peripheral iridotomy (LPI) dysphotopsias. METHODS: A surgical marking pen is applied to the area over a peripheral iridotomy before intrastromal keratopigmentation. The patient can then assess whether there is any improvement in their symptoms of dysphotopsias. Manual intrastromal keratopigmentation can then be performed using a crescent blade into the clear cornea at 50% depth and tunneled centrally to create a pocket ensuring that the peripheral iridotomy is fully occluded. The crescent blade is coated with an alcohol-based commercially available black tattoo pigment, and the pocket is filled. RESULTS: We have used the preoperative ink marker test on 5 eyes in patients with post-LPI (4 temporal and 1 superior) dysphotopsias before performing intrastromal keratopigmentation, with good patient satisfaction. Patients report immediate symptomatic relief after the procedure. This ink marking technique can also be extended to help identify which iris defect is symptomatic in patients with multiple iris defects. CONCLUSIONS: The preoperative ink test before intrastromal keratopigmentation is a novel adjunct to the treatment of post-LPI dysphotopsias.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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 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

Citations8
Published2017
Admission routes1
Has abstractyes

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