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Record W2061162559 · doi:10.1111/lam.12277

Identification of coagulase-negative staphylococci in daily disposable contact lens wearers

2014· article· en· W2061162559 on OpenAlexaff
Brad Hall, David J. McCanna, Lyndon Jones

Bibliographic record

VenueLetters in Applied Microbiology · 2014
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsContact lensOptometryLibrary scienceOphthalmologyMedicineArt historySociologyArtComputer science

Abstract

fetched live from OpenAlex

UNLABELLED: This study aimed to identify and quantify the number of contaminating organisms on daily disposable (DD) soft contact lenses, which may be responsible for mild cases of keratitis that occur with this lens wear modality. Ten participants wore DD lenses, and 10 participants wore planned replacement (PR) lenses. Lenses were collected aseptically and analysed for microbial contamination. Colony-forming units (CFU) were recorded, and representative colonies were used for identification using the API identification system. The DD lenses evaluated in this study were contaminated with coagulase-negative staphylococcus (CNS), ranging from 1 to 653 CFU. PR lenses showed more diversity in the types of contaminating micro-organisms and consisted of CNS, Gram-negative bacteria (Pseudomonas), a yeast (Candida) and a mould (Aspergillus), ranging from 1 to 230 CFU. CNS was the only type of micro-organism found on DD contact lenses and therefore may be the cause of any form of keratitis observed in DD lens wearers. SIGNIFICANCE AND IMPACT OF THE STUDY: This is the first study to determine the frequency and identify the contaminating organisms found on daily disposable (DD) soft contact lenses. The contaminating organisms identified on DD contact lenses were solely coagulase-negative staphylococcus (CNS), suggesting that CNS may be the causative organism associated with infectious keratitis that occurs with DD contact lens wear.

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)0.000
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 score0.054
Threshold uncertainty score0.643

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.006
GPT teacher head0.221
Teacher spread0.214 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

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