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Record W2884602532 · doi:10.4103/meajo.meajo_198_16

Outcome analysis of visual acuity and side effect after ruthenium-106 plaque brachytherapy for medium-sized choroidal melanoma

2018· article· en· W2884602532 on OpenAlexaff
Tahra AlMahmoud, Sean Quinlan-Davidson, Gregory R. Pond, Jean Deschênes

Bibliographic record

VenueMiddle East African Journal of Ophthalmology · 2018
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsOntario Clinical Oncology GroupMcMaster UniversityJuravinski Cancer CentreMcGill UniversityHamilton Health SciencesRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineBrachytherapyChoroidal melanomaVisual acuityRadiation therapyMelanomaRetrospective cohort studyOphthalmologySurgeryNuclear medicine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to report on treatment outcomes for medium-sized choroidal melanoma treated with Ruthenium-106 (Ru-106) plaque brachytherapy. METHODS: A retrospective case series of 28 patients received Ru-106 brachytherapy treatment for choroidal melanoma. The prescribed tumor dose was 85 Gy to a depth of 5 mm. RESULTS: Median follow-up was 31.2 months. At 12 and 24-month postirradiation, the best corrected visual acuity ≥20/70 (LogMar ≥-0.54) was 53.8% and 64.2%, respectively. Median time to tumor regression was estimated to be 10 months (95% CI = 9-18 months), with 100% of response rate by 32 months. Radiation-induced side effects were limited, and there were no postradiation enucleations. CONCLUSIONS: The majority of patients maintained good visual acuity, with no enucleations and minimal side effects. In this cohort, the Ru-106 plaque brachytherapy proved to be an efficacious and safe treatment option for patients with medium-sized choroidal melanomas with a maximal tumor height of 5 mm.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.031
GPT teacher head0.333
Teacher spread0.302 · 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 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
Published2018
Admission routes1
Has abstractyes

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