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Record W4379055908 · doi:10.22374/jclrs.v2isp1.30

IOP and Glaucoma

2018· article· en· W4379055908 on OpenAlexvenueno aff
Cherie B. Nau

Bibliographic record

VenueJournal of Contact lens Research and Science · 2018
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsnot available
FundersResearch to Prevent Blindness
KeywordsScleral lensGlaucomaOptometryOphthalmologyIntraocular pressurePopularityMedicineScleraEnthusiasmLens (geology)Intraocular lensesIntraocular lensPsychologyCorneaOptics

Abstract

fetched live from OpenAlex

Scleral lenses have steadily gained popularity and their efficacy in managing complex ocular disease is increasingly being recognized. They provide an exciting opportunity to allow us to improve vision and comfort in some of our most challenging patients. However, enthusiasm for the potential benefits of scleral lenses should not overshadow potential complications that could be caused by scleral lens wear. One specific area of concern is the possibility that scleral lenses may cause an increase in intraocular pressure (IOP).

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.027
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0270.008

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 head0.089
GPT teacher head0.400
Teacher spread0.311 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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