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Record W3012465945

Recent and current clinical trials in canine appendicular osteosarcoma.

2020· article· en· W3012465945 on OpenAlexaff
Andrew C. Poon, Arata Matsuyama, Anthony J. Mutsaers

Bibliographic record

VenuePubMed · 2020
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicineClinical trialAppendicular skeletonGynecologyOsteosarcomaRadiation therapyInternal medicineOncologyPathology
DOInot available

Abstract

fetched live from OpenAlex

Osteosarcoma (OSA) is an aggressive primary bone tumor in the domestic dog that most often occurs within the appendicular skeleton. Despite the use of adjuvant chemotherapy, most dogs succumb to metastatic disease within 1 year of diagnosis. To improve this outcome, substantial research is currently focused on investigating novel therapies. Herein, we review emerging treatments and clinical trials that, if proven efficacious, could revolutionize the standard of care for canine appendicular OSA. This article includes a critical perspective on the safety, efficacy, and limitations of select immunotherapy, virotherapy, radiotherapy, targeted therapy, and personalized medicine trials, all of which reflect similar investigations taking place in human oncology. These clinical trials represent a major evolution in the overall approach to therapy for dogs with appendicular OSA that could have significant implications for improving survival.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.449
GPT teacher head0.494
Teacher spread0.045 · 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
GenreReview

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

Citations25
Published2020
Admission routes1
Has abstractyes

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