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Record W3123702222 · doi:10.1097/ijg.0000000000001701

Analysis of Efficacy and Safety of Pediatric Ahmed Glaucoma Valve (FP8) in Advanced Age Populations

2020· article· en· W3123702222 on OpenAlexaff
Paul Crichton, Emi Sanders, Gavin Docherty, Andrew Crichton

Bibliographic record

VenueJournal of Glaucoma · 2020
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineGlaucomaGlaucoma valveOptometryOphthalmology

Abstract

fetched live from OpenAlex

PRECIS: The FP8 glaucoma valve was demonstrated to be reasonably safe with reliable results in an advanced age patient population. PURPOSE: As life expectancy increases, a growing number of patients with glaucoma are of an advanced age. There are little to no data looking at glaucoma surgical treatment options in patients over the age of 85. Our study describes the safety and efficacy of the FP8 Ahmed glaucoma valve in this patient population. MATERIALS AND METHODS: This was a retrospective study of patients over 85 years of age undergoing FP8 Ahmed glaucoma valve implantation. Preoperative age, sex, intraocular pressure (IOP), and number of glaucoma medications were recorded. Primary outcome variables were IOP and number of medications. Secondary outcome variables included any intraoperative or postoperative complications. RESULTS: Mean IOP preoperatively was 26 mm Hg on an average of 3 glaucoma medications (n=56). IOP was significantly reduced at all time points in follow-up for an overall reduction of 42% at 1 year and 46% at 2 years. Mean IOP at 1 year follow-up was 15 mm Hg and 14 mm Hg at 2 years follow-up. Glaucoma medications were reduced from a mean of 3 preoperatively to 2 postoperatively. CONCLUSIONS: Implantation of an FP8 Ahmed glaucoma valve is a relatively safe procedure to achieve satisfactory IOP and decreased reliance on glaucoma medications in an advanced age population.

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 score0.003
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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