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Record W4289856742 · doi:10.2147/opto.s350186

Eyelid Warming Devices: Safety, Efficacy, and Place in Therapy

2022· review· en· W4289856742 on OpenAlexaff
Brandon Bzovey, William Ngo

Bibliographic record

VenueClinical Optometry · 2022
Typereview
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEyelidMedicineOphthalmology

Abstract

fetched live from OpenAlex

Meibomian gland dysfunction (MGD) is characterized by the obstruction and/or inflammation of the meibomian glands that result in decreased and altered meibum secretion. This results in deficiencies in the tear film lipid layer which contributes to increased evaporation and destabilization of the tear film. One of the mainstay therapies for MGD is medical devices that apply heat and/or pressure to the eyelids and promote the liquification and outflow of meibum into the tear film. Over the past two decades, there have been a surge of interest in diagnosing and managing MGD. As a result, numerous medical devices have been developed and each have their own unique approach to treating MGD. This narrative review was conducted to summarize the current state of knowledge on eyelid warming devices, specifically warm eye coverings, devices that direct heat and/or pressure to the eyelids, moisture chamber goggles, and light-based therapy. This review summarized 58 human clinical studies and found that most eyelid warming devices were efficacious in improving signs and symptoms in a wide range of MGD severities and were generally safe to use.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.126
GPT teacher head0.479
Teacher spread0.352 · 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 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

Citations9
Published2022
Admission routes1
Has abstractyes

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