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Record W2027358475 · doi:10.1097/opx.0b013e31804636af

Targeting Contact Lens Induced Dryness and Discomfort: What Properties Will Make Lenses More Comfortable

2007· review· en· W2027358475 on OpenAlexaff
Desmond Fonn

Bibliographic record

VenueOptometry and Vision Science · 2007
Typereview
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDrynessContact lensOptometryLens (geology)MedicineOphthalmologyOpticsSurgery

Abstract

fetched live from OpenAlex

There are about 35 million contact lens wearers in North America, and about half of them are symptomatic of dryness and discomfort, more commonly experienced at the end of the lens wearing day. Most of these contact lens wearers do not suffer from true dry eye, which is a pervasive and ubiquitous disease or condition that affects many millions of people in North America. It seems fairly clear that the lens causes the eye to become uncomfortable or "dry," and unless it is effectively remedied, the patient will most likely discontinue lens wear. This review describes the dryness and discomfort effects of contact lenses and how contact lens properties with novel solutions have the potential for eliminating or alleviating these symptoms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.063
GPT teacher head0.438
Teacher spread0.375 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations157
Published2007
Admission routes1
Has abstractyes

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