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Record W1974003402 · doi:10.1097/icb.0b013e31817f2c80

OCULAR ISCHEMIA IN HIGH MYOPIA TREATED WITH INTRAVENOUS PROSTAGLANDIN E1

2009· article· en· W1974003402 on OpenAlexaff
Robert D. Steigerwalt, M R Cesarone, Gianni Belcaro, Antonella Pascarella, Laura Rapagnetta, Mauro De Angelis, Marcella Nebbioso

Bibliographic record

VenueRetinal Cases & Brief Reports · 2009
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsMonsanto (Canada)
Fundersnot available
KeywordsMedicineIschemiaVisual acuityProstaglandin E1MicrocirculationOphthalmologyBlood flowProstaglandinInternal medicine

Abstract

fetched live from OpenAlex

In Brief Background: High myopia is associated with a decreased ocular blood flow. In some cases this ocular ischemia may be the cause of severe visual loss. Methods: Three patients with high myopia and progressive loss of visual acuity had a diagnosis of ocular ischemia by color Doppler. Intravenous prostaglandin E1, a powerful vasodilator of the microcirculation, was used to treat the ocular ischemia in all 3 patients. Results: The visual acuity improved in all three cases with one patient improving from 20/100 to 20/30. The mean deficit of the visual fields in this patient improved from −19.08 to −9.52 after treatment. The treatment was repeated every 6 weeks to 8 weeks. Conclusion: Patients with high myopia and progressive visual acuity loss should be evaluated for ocular ischemia. Intravenous prostaglandin E1 should be considered in those cases of ocular ischemia with visual loss. Unfortunately the effect does not last for more than 6 weeks to 8 weeks and needs to be repeated at this interval for extended periods. Three patients with high myopia and progressive loss of visual acuity had color Doppler imaging revealing ocular ischemia. They were treated with intravenous prostaglandin E1 for their ischemia with visual improvement. The effect of the treatment can last for up to 8 weeks at which time it must be repeated.

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 score0.114
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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