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Record W1919588364 · doi:10.1111/vco.12121

Hypofractionated radiation therapy for the treatment of microscopic canine soft tissue sarcoma

2014· article· en· W1919588364 on OpenAlexaff
Margret Kung, Valérie J. Poirier, Michelle M. Dennis, David M. Vail, Rodney C. Straw

Bibliographic record

VenueVeterinary and Comparative Oncology · 2014
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Guelph
FundersUniversity of Queensland
KeywordsSoft tissueRadiation therapySoft tissue sarcomaSarcomaMedicineRadiologyPathology

Abstract

fetched live from OpenAlex

Soft tissue sarcomas (STSs) are locally invasive and surgery with or without radiation therapy is the current standard of care in dogs. Typical protocols for treating incompletely excised STSs involve curative intent radiation with total dose in excess of 50 Gy. Forty-eight dogs with histologically confirmed incomplete or closely excised STSs were treated with a hypofractionated protocol that is typically reserved for palliative radiation therapy (RT) (6-8 Gy/weekly fractions to a total dose of 24-32 Gy). Ten dogs (21%) developed local recurrence, 11 dogs (23%) developed metastasis, and 3 dogs developed both (included in each group). The median progression free survival was 698 days. The local failure-free probability at 1 and 3 years was 81 and 73%. The 1 and 3 years tumour-specific overall survival was 81 and 61%. Long-term local tumour control was achieved in the majority of dogs. This protocol is reasonable to prescribe in older patients or when financial limitations exist.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.150
GPT teacher head0.444
Teacher spread0.294 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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