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Record W2285522374 · doi:10.3928/1081-597x-20061102-12

Correction of High Astigmatism: Case Studies Using the Mixed-cylinder Approach

2006· article· en· W2285522374 on OpenAlexaff
Hamza N. Khan, Geoffrey B Kaye, Jean Luc Febbraro

Bibliographic record

VenueJournal of Refractive Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsAstigmatismCylinderExcimer laserExcimerOphthalmologyMedicineLaserOpticsOptometryMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

PURPOSE: To explain the use of the mixed-cylinder approach in treating moderate to high astigmatism with the NIDEK EC-5000 excimer laser system. METHODS: Retrospective case series report. RESULTS: Three patients with bilateral moderate to high astigmatism were treated successfully using the mixed cylinder approach. CONCLUSIONS: The use of the mixed-cylinder approach with the NIDEK EC-5000 excimer laser may be a safe and predictable option for treating moderate to high astigmatism.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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 designCase report
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

Citations5
Published2006
Admission routes1
Has abstractyes

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