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Record W2616695697 · doi:10.1097/opx.0000000000001082

Comparison of Ocular Lubricant Osmolalities

2017· article· en· W2616695697 on OpenAlexafffund
Etty Bitton, Carolyn Perugino, Stéphanie Charette

Bibliographic record

VenueOptometry and Vision Science · 2017
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsAssociation for Canadian StudiesUniversité de Montréal
FundersCanadian Optometric Education Trust Fund
KeywordsOsmometerLubricantOsmoleChemistryChromatographyGuar gumOphthalmologyRelative humidityArtificial tearsMedicineFood scienceInternal medicineOrganic chemistry

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to evaluate the osmolality of commercially available ocular tear lubricants. METHODS: Thirty-seven (n = 37) ocular lubricants, measured three times each, were evaluated for osmolality using a vapor pressure osmometer (Wescor VAPRO 5520). The osmometer was calibrated before each use, and the order of the lubricants was randomized. Ambient temperature and humidity were monitored for stability. RESULTS: Of the 37 ocular lubricants tested, 35 (94.6%) had an osmolality of less than 295 mmol/kg, one (2.7%) had between 295 and 308 mmol/kg, and one (2.7%) had more than 308 mmol/kg. The ambient room temperature was stable and ranged from 21.9°C to 22.0°C, and the relative humidity ranged from 21.2% to 25.6% during experimentation. When ocular lubricants were grouped by ingredient (carboxymethylcellulose and hydroxylpropyl methylcellulose, hyaluronic acid, and hydroxypropyl guar), no significant difference in osmolality was noted between groups (Mann-Whitney U test, P > .05). CONCLUSIONS: The majority of the ocular lubricants tested had low osmolalities, mimicking the osmolarity of newly formed tears (295 to 300 mOsm/L). Several factors need to be considered when choosing a tear lubricant, which have more complex formulations than ever. Knowledge of their osmolality may be an added parameter to consider when choosing therapeutic options for dry eye.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.313

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 studies0.0000.001
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 head0.036
GPT teacher head0.481
Teacher spread0.445 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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