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Record W2800538656 · doi:10.22374/jclrs.v2i1.21

Compliance Using Scleral Lenses

2018· article· en· W2800538656 on OpenAlexvenueno aff
Daddi Fadel, Mindy Toabe

Bibliographic record

VenueJournal of Contact lens Research and Science · 2018
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsnot available
Fundersnot available
KeywordsCompliance (psychology)HygieneMedicineContact lensPatient complianceModalitiesEmpathyPatient safetyScleral lensIntensive care medicinePatient careOptometryMedical emergencyHealth careNursingPsychologyOphthalmologyFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

The rise in scleral contact lens (ScCL) use may increase the risk of complications including microbial keratitis. Patient understanding of hygiene as well as care and handling of ScCLs is crucial to prevent the increase of complications. This manuscript presents a review of the modalities and implications of the hygiene and the care of scleral lenses, storage case, and devices. Also, correct application and removal techniques are explained in detail and recommendations on instructions for ScCL wearers and verification of patient compliance are provided. Adverse events are rarely reported in literature but clinicians need to understand how to deliver appropriate information and instruction regarding basic rules of hygiene, contact lens care and handling to patients to prevent and minimize associated infections. It is fundamental that practitioners learn how to impart knowledge to their patients during every encounter including follow up visits regarding proper management and compliance with ScCLs. Also, a dialogue with the patient is essential to develop empathy allowing for greater collaboration and compliance raising the fitting rate, success and patient’s overall satisfaction.

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)0.004
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.272
GPT teacher head0.454
Teacher spread0.182 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Contact lens Research and ScienceSame topicOcular Surface and Contact LensFrench-language works237,207