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Record W2922353454 · doi:10.5206/uwomj.v84i2.4299

The implantable miniature telescope

2015· article· en· W2922353454 on OpenAlexvenueaboutno aff
Phillip Williams, Steven Chan-Fai Wong

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMacular degenerationBlindnessFood and drug administrationVisual acuityQuality of life (healthcare)OphthalmologyStage (stratigraphy)OptometrySurgeryMedical emergency

Abstract

fetched live from OpenAlex

Bilateral, end-stage, age-related macular degeneration (AMD) is a devastating condition of the eye. As the leading cause of blindness worldwide in the elderly, it leads to poor quality of life. While antivascular endothelium growth factor agents are used as front-line treatment for wet AMD, no current treatment exists for bilateral, end-stage AMD in Canada. The implantable miniature telescope (IMT), approved by the Food and Drug Administration in 2010, is a treatment option available for those over the age of 65 in the United States with stable severe to profound vision impairment (best-corrected visual acuity 20/160 to 20/800) caused by bilateral central scotomas associated with bilateral, end-stage AMD. Combining the fields of engineering and ophthalmology, intraocular implantation of the IMT provides improvements in patients’ functional vision and quality of life. As a relatively new treatment targeting the elderly, there are strict inclusion criteria as well as risks associated with the procedure. However, research and continuing development in this field is ongoing to minimize these risks.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.004

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 head0.017
GPT teacher head0.232
Teacher spread0.215 · 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 designBench or experimental
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

Citations3
Published2015
Admission routes2
Has abstractyes

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