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Record W1174728981 · doi:10.1001/archopht.124.10.1472

Effect of Medical Therapy on Glaucoma Filtration Surgery Rates in Ontario

2006· article· en· W1174728981 on OpenAlexaffabout
Rony Rachmiel

Bibliographic record

VenueArchives of Ophthalmology · 2006
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsGlaucomaMedicineMedical therapyOphthalmologyFiltration (mathematics)OptometrySurgeryMathematicsStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To analyze trends of glaucoma filtration surgery in Ontario. METHODS: From April 1, 1992, through March 31, 2004, correlations were examined between the annual rates of trabeculectomies in Ontario, the use of glaucoma medications, and the numbers of practicing ophthalmologists and optometrists. RESULTS: The number of trabeculectomies per 1000 persons at risk for primary open-angle glaucoma increased from 33.5 in 1992 to 46.2 in 1996 (37.7% increase; 6.6% increase per year) and then steadily decreased to 38.2 in 2004 (17.0% decrease; 2.7% decrease per year). The number of glaucoma medications dispensed in Ontario increased from 766 000 in 1992 to 1 466 543 in 2004 (91.5% increase; 10.5% annual increase). The increase in dispensed prostaglandin analogues strongly correlated (P<.001; 95% confidence interval, -0.87 to -0.41) with the decreasing number of trabeculectomies. The decreasing number of ophthalmologists positively correlated (r = 0.87) with the filtration surgery rate after 1997. CONCLUSIONS: The number of trabeculectomies has decreased substantially in Ontario coinciding with the introduction of medications for the treatment of glaucoma in December 1996. This decrease in trabeculectomies highly correlated with the introduction of prostaglandin analogues (P<.001) and the decreasing number of ophthalmologists from 1997 through 2004.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.026
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.015
GPT teacher head0.291
Teacher spread0.277 · 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
Published2006
Admission routes2
Has abstractyes

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