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Record W1973829168 · doi:10.1097/icu.0b013e3283366a66

Management of coexisting cataract and glaucoma

2010· review· en· W1973829168 on OpenAlexaff
Andrew Crichton

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2010
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineGlaucomaCataract surgeryVariety (cybernetics)OptometryIntensive care medicineOphthalmologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The optimal management of cataract in the glaucoma patient must be constantly updated as advances in both subspecialties impact the surgical decision. RECENT FINDINGS: The primary considerations at this time include combining the procedures as opposed to sequential operations and optimizing the effectiveness of the operations while reducing complications. SUMMARY: Recent findings would certainly suggest an individualized approach based not only on the glaucoma and cataract condition of the patient but also on the experience of the surgeon with varied techniques. Further time and research are required to determine whether certain approaches or techniques will become more universally adopted. At present, there certainly is support for a variety of approaches.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.108
GPT teacher head0.433
Teacher spread0.325 · 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
GenreReview

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

Citations10
Published2010
Admission routes1
Has abstractyes

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