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Record W1556053938 · doi:10.1097/opx.0b013e31820efa0f

Survey of Contact Lens Prescribing to Infants, Children, and Teenagers

2011· article· en· W1556053938 on OpenAlexaff
Nathan Efron, Philip B. Morgan, Craig A. Woods

Bibliographic record

VenueOptometry and Vision Science · 2011
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOrthokeratologyContact lensMedicineOptometryLens (geology)PediatricsDemographyOphthalmologyCornea

Abstract

fetched live from OpenAlex

PURPOSE: To determine the types of contact lenses prescribed for infants (aged 0 to 5 years), children (6 to 12 years), and teenagers (13 to 17 years) around the world. METHODS: Up to 1000 survey forms were sent to contact lens fitters in each of 38 countries between January and March every year for 5 consecutive years (2005 to 2009). Practitioners were asked to record data relating to the first 10 contact lens fits or refits performed after receiving the survey form. RESULTS: Data were received relating to 105,734 fits [137 infants, 1,672 children, 12,117 teenagers, and 91,808 adults (age ≥ 18 years)]. The proportion of minors (<18 year old) fitted varied considerably between nations, ranging from 25% in Iceland to 1% in China. Compared with other age groups, infants tend to be prescribed a higher proportion of rigid, soft toric, and extended wear lenses, predominantly as refits for full-time wear, and fewer daily disposable lenses. Children are fitted with the highest proportion of daily disposable lenses and have the highest rate of fits for part-time wear. Teenagers have a similar lens fitting profile to adults, with the main distinguishing characteristic being a higher proportion of new fits. Orthokeratology fits represented 28% of all contact lenses prescribed to minors. CONCLUSIONS: Patterns of contact lens prescribing to infants and children are distinctly different to those of teenagers and adults in a number of respects. Clinicians can use the data presented here to compare their own patterns of contact lens prescribing to minors.

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)0.000
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 score0.030
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.032
GPT teacher head0.370
Teacher spread0.337 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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