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Relative efficacy of intracameral moxifloxacin injection methods

2023· article· en· W4319301732 on OpenAlexaffabout
Steve A. Arshinoff, Runjie Bill Shi

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2023
Typearticle
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMoxifloxacinMixing (physics)BottleComputer scienceMedicineBiomedical engineeringMaterials scienceChemistryPhysics

Abstract

fetched live from OpenAlex

PURPOSE: To determine the amount of moxifloxacin remaining in the anterior chamber (AC), immediately after its injection using 3 current injection methods, assuming mixing and fluid exchange with the AC contents during injection of the drug, and to determine the most desirable injection method. SETTING: Department of Ophthalmology and Vision Sciences and Institute of Biomedical Engineering, University of Toronto, Toronto, Canada. DESIGN: Mathematical modeling. METHODS: Mathematical modeling using first-order mixing methods were used to assess mixing. RESULTS: The Kaiser method of injecting 0.5 mL × 100 μg/0.1 mL does not achieve the desired 500 μg level of moxifloxacin in the AC. The "straight from the bottle" method of injecting 0.1 mL × 500 μg/0.1 mL is fraught with potential error, yielding a relatively unreliable final amount in the AC. Injecting 0.5 to 0.6 mL × 150 μg/0.1 mL yields a result closest to the desired goal. CONCLUSIONS: Based on the calculation, the most accurate of current methods to deliver 500 μg moxifloxacin intracamerally is the method of 150 μg/0.1 mL × 0.5 to 0.6 mL.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
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 head0.041
GPT teacher head0.398
Teacher spread0.357 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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