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Record W2801888496

Emmetropia: Why we should do immediately sequential bilateral cataract surgery

2010· article· en· W2801888496 on OpenAlexaboutno aff
Steve A. Arshinof

Bibliographic record

VenueJournal of Emmetropia: Journal of Cataract, Refractive and Corneal Surgery · 2010
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsnot available
Fundersnot available
KeywordsEmmetropiaMedicineContext (archaeology)Cataract surgeryOptometrySurgeryVisual acuityRefractive error
DOInot available

Abstract

fetched live from OpenAlex

PURPOSE: To research and discuss the reasoning behind the performance of immediately sequential bilateral cataract surgery (ISBCS) in the context of achieving emmetropia and visual restoration. SETTING: York Finch Eye Associates, Toronto, Canada METHODS: A literature review of pertinent articles and books relevant to visual function and bilateral cataract surgery. The website of the International Society of Bilateral Cataract Surgeons (ISBCS) was also reviewed. RESULTS: The logic of human destiny and progress dictates that we will move to ISBCS to achieve better restoration of vision than achievable with delayed sequential bilateral cataract surgeries (DSBCS). ISBCS restores the visual system, whereas DSBCS can be an impediment to binocularity. Caution is required in moving to ISBCS, and reasonably strict protocols should be observed. The additional cost of performing DSBCS to a health care system is unacceptable for the miniscule additional risk of ISBCS. DISCUSSION: As we move to ever more predictable surgical results, routinely achieving emmetropia, monovision, multifocality or pseudoaccomodation, the further enhancement of our surgeries with ISBCS is often perceived by the patient as the greatest of all recent advances in cataract surgery

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)0.006
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.009
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.296
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2010
Admission routes1
Has abstractyes

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