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Record W2806991827 · doi:10.1159/000487543

The Use of Intravitreal Anti-VEGF and Triamcinolone in the Treatment of Radiation Papillopathy

2018· article· en· W2806991827 on OpenAlexaff
Kelsey A. Roelofs, Albert Murtha, Ezekiel Weis

Bibliographic record

VenueOcular Oncology and Pathology · 2018
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineBevacizumabTriamcinolone acetonideOphthalmologyVisual acuitySurgeryChemotherapy

Abstract

fetched live from OpenAlex

<b><i>Background/Aims:</i></b> To evaluate a treatment regimen for radiation papillopathy. <b><i>Methods:</i></b> This is a prospective noncomparative interventional case series of patients who developed radiation papillopathy after plaque brachytherapy for uveal melanoma. Treatment consisted of intravitreal bevacizumab (IVB) (1.25 mg in 0.05 mL) at the time of diagnosis, and 1 week later, intravitreal triamcinolone (IVK) (2.00 mg in 0.05 mL). One month later, patients again received both IVB and IVK. Patients were then switched to monotherapy with monthly IVB until the papillopathy resolved. <b><i>Results:</i></b> Eleven patients developed radiation papillopathy, with 9 receiving treatment. On multivariate analysis, total radiation dose to the optic nerve was the only significant predictor of papillopathy (<i>p</i> = 0.005). Four of nine patients presented with a significant decline in visual acuity (VA) at the time of diagnosis of papillopathy. In all 4, significant improvement was documented following treatment. Five patients did not present with a decrease in VA, and in this group, 80% remained stable with treatment. <b><i>Conclusions:</i></b> In this series, patients who had a precipitous drop in VA at the time of diagnosis of radiation papillopathy returned to baseline VA following this treatment algorithm. This protocol was effective at maintaining stability of VA in 80% of those who developed papillopathy but did not present with an acute drop in VA.

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 score0.400
Threshold uncertainty score0.390

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 studies0.0000.001
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 head0.041
GPT teacher head0.320
Teacher spread0.279 · 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

Citations23
Published2018
Admission routes1
Has abstractyes

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