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Record W4244285467 · doi:10.17925/usor.2015.8.1.14

Immediately Sequential Bilateral Cataract Surgery—A Global Perspective

2015· article· en· W4244285467 on OpenAlexaff
Steve A. Arshinoff

Bibliographic record

VenuetouchREVIEWS in Ophthalmology · 2015
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsHumber River Regional HospitalMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineCataract surgeryScrutinyExcellenceIntraocular lensOptometryEndophthalmitisPerspective (graphical)SurgeryPolitical scienceLaw

Abstract

fetched live from OpenAlex

By the mid 1990s, the evolution of progressively safe and predictable cataract surgery saw the resurgence of centuries of interest in performing immediately sequential bilateral cataract surgery (ISBCS). Within 10 years ISBCS had become generally accepted and the International Society of Bilateral Cataract Surgeons (iSBCS) was formed (September 2008) to “promote education, mutual cooperation, and progress in simultaneous bilateral cataract surgery.” The first initiative of the society members was to create a document “iSBCS General Principles for Excellence in ISBCS 2009,” which was intended to disseminate information about the best practices they had discovered to assist novice ISBCS surgeons. Next, ISBCS needed to be studied. Soon, data began to clarify some advantages of ISBCS, and risks were carefully evaluated. It soon became apparent that the main impediment to the performance of ISBCS, globally, was money, in that many jurisdictions financially penalized surgeons who performed ISBCS. Presently, we know that ISBCS carries many benefits to the patient, his/her family, the surgical facility, and society. The feared risks for simultaneous bilateral endophthalmitis, the requirement to adjust intraocular lens (IOL) selection for second eyes, based on first eye results, and others, have simply not been borne out under scrutiny. ISBCS is now rapidly increasing in its performance and acceptance globally, but the financial factors remain to be solved.

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)0.001
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 score0.321
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.156
GPT teacher head0.401
Teacher spread0.244 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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