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Role of rheology in tears and artificial tears

2020· review· en· W3112161615 on OpenAlexaff
Steve A. Arshinoff, Ilan Hofmann, H. N. Naé

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsEnvironment and Climate Change CanadaArup Group (Canada)
Fundersnot available
KeywordsRheologyTearsArtificial tearsViscoelasticityShear thinningMaterials scienceOphthalmologyMedicineSurgeryComposite material

Abstract

fetched live from OpenAlex

The study of viscoelastic fluids as artificial tears dates back to the late 1970s. Healon, the first ophthalmic viscosurgical device, was approved in 1980, but studied extensively before then, exhibits very interesting shear-thinning properties that were found to be beneficial in both ophthalmic surgery and somewhat later as a tear replacement solution. Unlike the previous tear film replacements, which were mainly viscous in nature, viscoelastic solutions, particularly those based on hyaluronan, exhibited very interesting, potentially beneficial, rheological properties, especially when slightly altered to become elastoviscous. This review examines the rheological properties that are significant in artificial tear solutions. We define herein the necessary parameters that need to be further studied to design and formulate rheologically better artificial tears, which should provide enhanced efficacy compared with their predecessors.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.033
GPT teacher head0.325
Teacher spread0.292 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations32
Published2020
Admission routes1
Has abstractyes

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