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Record W2105612280 · doi:10.1017/s0265021506002018

Levobupivacaine 0.75% vs. lidocaine 4% for topical anaesthesia

2007· article· en· W2105612280 on OpenAlexaff
A. Di Donato, Carlos Eduardo Fontana, F. Lancia, K. Di Giorgio, S. Reali, Alessio Caricati

Bibliographic record

VenueEuropean Journal of Anaesthesiology · 2007
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsConcordia Hospital
Fundersnot available
KeywordsMedicineLevobupivacaineLidocaineAnesthesiaBupivacaine

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to compare the efficacy of topical levobupivacaine drops 0.75% vs. lidocaine drops 4% in cataract surgery. METHODS: We examined 203 patients undergoing cataract surgery by phacoemulsification. They were randomized into two groups: one received four drops of lidocaine 4% and the other received four drops of levobupivacaine 0.75%. The onset and offset times of sensory block were evaluated. Application, intraoperative and postoperative subjective pain was quantified by the patients using a verbal pain score. Complications, rates of supplemental anaesthesia, and the satisfaction of surgeon and patients were also recorded. RESULTS: The mean sensory onset and offset times were significantly higher for the levobupivacaine group (P < 0.01). Pain score was lower in the levobupivacaine group than in the lidocaine one and the difference was statistically significant at all stages (P < 0.01). The mean satisfaction scores of patients and surgeon were also statistically higher for levobupivacaine (P < 0.01). No significant differences for complications and rates of supplemental anaesthesia were found. CONCLUSIONS: Topical levobupivacaine 0.75% shows the same efficacy and safety as lidocaine 4% in cataract surgery by phacoemulsification. There was an adequate block with a good level of satisfaction of surgeon and patients. Levobupivacaine 0.75% offers a new and acceptable choice for topical anaesthesia in cataract 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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.528

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.023
GPT teacher head0.278
Teacher spread0.255 · 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 designOther design
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

Citations9
Published2007
Admission routes1
Has abstractyes

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