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Global practice patterns and outcomes for metastatic uveal melanoma treated regionally versus systemically.

2025· article· en· W4410814439 on OpenAlexaffabout
Marcus O. Butler, Joseph J. Sacco, Shaheer Khan, Marlana Orloff, Sapna P. Patel, Ryan J. Sullivan, Alexander N. Shoushtari, Brian P. Marr, Mark Shackleton, CarolL Shields, Hatem Krema, Li‐Anne Lim, Max Conway, Leah Young, Femida Gwadry‐Sridhar, Anthony M. Joshua, Richard D. Carvajal

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineMelanomaMetastatic melanomaOncologyInternal medicineCancer research

Abstract

fetched live from OpenAlex

e23324 Background: Due to the hepatotropic pattern of metastasis in uveal melanoma (UM), liver directed therapies (LDT) are commonly used for the treatment of advanced disease. The PUMMA meta-analysis (Khoja et al, 2019) assessed patients (pts) with metastatic UM (mUM) treated on trials conducted from 2000-2016 and suggested improved outcomes for those treated with LDT. Methods: Using data from 7 centers in the US, Canada, the UK and Australia collected between as part of the Ocular Melanoma Natural History (OMNi) study, we assessed this finding in a more contemporary dataset and investigated potential global variations in practice patterns. Analysis was performed on data entered as of May 2024. Results: Of 985 pts enrolled, 283 developed mUM and received at least 1 line of treatment (tx; 77-US; 160-Canada; 30-UK; 16-Australia). 164 received regional (R) tx (LDT, surgery, radiation; 41% US pts, 35% Canadian pts, 46% UK pts, 55% Australian pts), with 130 treated in the first-line (1L) setting. 212 received systemic (S) tx (51% US pts, 55% Canadian pts, 51% UK pts, 55% Australian pts), with 137 treated in the 1L setting. Concurrent (C; any R tx delivered while a S tx was ongoing) was administered in 50 patients (18% US pts, 10% of Canada pts, 3% UK, pts 5% Australian pts), with 16 treated in the 1L setting. At the time of 1L tx, median age was 63 (range, 25-92), 62 (range, 24-88), and 64 (range, 42-83) years for those treated with S, R, and C tx, respectively. 47%, 50% and 25% were female of those treated with S, R, and C tx, respectively. Mean diameter of the largest tumor lesion and percentage of cases with stage M1b/c disease at time of 1L tx were 1.5cm and 20%, 1.1cm and 19%, and 0.6 cm and 0% for those treated with S, R or C tx, respectively. There was no significant difference in proportion of patients with liver-only disease who received 1L S or R/C tx (61 vs 62%, respectively). Median overall survival (OS) was 32, 24 and 32 months for those treated with S, R or C tx, respectively, with no significant difference observed across groups (p = 0.09). Conclusions: The use of C tx was more common in the US and Canadian centers when compared with those in the UK and Australia. Patient and tumor characteristics were similar between those treated with 1L S and R therapies, with no difference in OS observed in this dataset based upon initial tx strategy. Clinical trial information: NCT04588662 . Therapy Pts Receiving 1L Systemic Therapy (n = 137) Pts Receiving 1L Regional Therapy (n = 130) Pts Receiving 1L Concurrent Therapy (n = 16) Systemic Regional Checkpoint Blockade 104 (76%) n/a 10 (62%) n/a Targeted Therapy 22 (16%) n/a 6 (38%) n/a Other Systemic Therapy 11 (8%) n/a 0 (0%) n/a Surgery or Radiofrequency Ablation n/a 62 (48%) n/a 3 (18%) Radiotherapy n/a 22 (17%) n/a 4 (25%) Other Liver Directed Therapy n/a 34 (26%) n/a 6 (38%) Radioembolization or Chemoembolization n/a 9 (7%) n/a 2 (12%) Percutaneous Hepatic Perfusion n/a 3 (2%) n/a 1 (6%)

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)0.002
metaresearch head score (Gemma)0.014
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.114
GPT teacher head0.512
Teacher spread0.399 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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