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Record W2029718604 · doi:10.1016/s0886-3350(03)00613-8

Effect of retrobulbar injection of lidocaine on saccadic velocities

2004· article· en· W2029718604 on OpenAlexaffabout
Elizabeth L. Irving, Steve A. Arshinoff, William Samis, Linda Lillakas, Byron Lui, Janet Taylor Laporte, Martin J. Steinbach

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2004
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsToronto Western HospitalHumber River Regional HospitalYork UniversityUniversity of TorontoUniversity of Waterloo
Fundersnot available
KeywordsLidocainePhacoemulsificationSaccadic maskingMedicineExtraocular musclesAnesthesiaIntraocular lensIntraocular surgeryCataract surgeryOphthalmologySurgeryEye movementVisual acuity

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether exposing the extraocular muscles (EOMs) to lidocaine via retrobulbar injection for cataract surgery has a demonstrable negative effect on subsequent function of the muscle. SETTING: York Finch Eye Associates, Humber River Regional Hospital, and Toronto Western Hospital Research Institute, Toronto, Ontario, Canada. METHODS: This study comprised 37 eyes that had phacoemulsification and posterior chamber intraocular lens implantation; 13 eyes had retrobulbar lidocaine with hyaluronidase and 24 eyes, topical anesthesia. The postoperative saccadic velocities were compared with the preoperative velocities using a sensitive recording device. The results were compared within and between the retrobulbar lidocaine and topical anesthesia groups. RESULTS: No detectable decrement in postoperative saccadic velocities was detected in any patient, and no difference was found between the groups. CONCLUSIONS: Exposing EOMs to lidocaine for cataract surgery had no detectable negative effect on saccadic velocities 1 week after 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)0.002
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.278
Teacher spread0.268 · 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

Citations7
Published2004
Admission routes2
Has abstractyes

Explore more

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