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Record W4401764133 · doi:10.1089/jop.2024.0071

Long-Term Stability, Sterility, And Cost-Effectiveness of 0.05% Chlorhexidine Gluconate as Antisepsis for Intravitreal Injection

2024· article· en· W4401764133 on OpenAlexaff
Asad F. Durrani, Bita Momenaei, Viren Soni, Matthew Tennant, Jason Hsu, J.F. Vander, Marc J. Spirn, Eugene Yu-Chuan Kang, Yih-Shiou Hwang, Gagan Kaushal, Sunir J. Garg

Bibliographic record

VenueJournal of Ocular Pharmacology and Therapeutics · 2024
Typearticle
Languageen
FieldMedicine
TopicOcular Infections and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChlorhexidine gluconateSterilityChlorhexidineMedicineSurgeryTerm (time)DentistryBiology

Abstract

fetched live from OpenAlex

Purpose: Commercially available chlorhexidine gluconate (CHG) has a beyond-use date of 24 h. This study evaluated the stability and sterility of 0.05% CHG for 30 days after opening and compared its cost to povidone iodine (PI) for intravitreal injection antisepsis. Methods: 0.05% CHG was aliquoted into 1-mL syringes and stored at room temperature or refrigerated. Turbidity, pH, high-performance liquid chromatography (HPLC), and sterility testing were performed. A cost analysis was conducted. Results: 0.05% CHG remained stable for at least 30 days. All samples had measured turbidity <0.5 nephelometric turbidity units. The pH of all samples remained between 5.0 and 7.0. HPLC demonstrated CHG concentration at day 30 relative to day 0 of 98.52% ± 4.16% at room temperature and 99.99% ± 3.38% at 2°C –6°C. The cost per week to perform 150 injections using 0.05% CHG was $463.25 when opening a new bottle daily compared with $16.73 for 5% PI. This cost decreased to $23.16 when utilizing a bottle of CHG for 30 days. Conclusion: 0.05% CHG remains stable and sterile for at least 30 days after opening. The ability to use CHG for at least 30 days after its opening significantly decreases its utilization expense.

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)0.000
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 score0.131
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.049
GPT teacher head0.398
Teacher spread0.348 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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