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Record W2334518704 · doi:10.1111/vco.12194

Clinical presentation, treatment and outcome in 31 dogs with presumed primary colorectal lymphoma (2001–2013)

2016· article· en· W2334518704 on OpenAlexaff
Isabelle Desmas, Jenna H. Burton, Gerald Post, Orna Kristal, M. Gauthier, Juan Borrego, Andrea Di Bella, Ana Lara‐Garcia

Bibliographic record

VenueVeterinary and Comparative Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsOakville-Trafalgar Memorial Hospital
Fundersnot available
KeywordsMedicineChemotherapyLymphomaInternal medicineRetrospective cohort studyPresentation (obstetrics)Radiation therapySurgeryImmunophenotypingGastroenterologyImmunologyAntigen

Abstract

fetched live from OpenAlex

The objective of this multicentre retrospective study was to describe clinical presentation, treatment and outcome and to determine prognostic factors for dogs with presumed primary colorectal lymphoma (PCRL). A total of 31 dogs were included. The predominant features of PCRL were high grade (n = 18) and immunophenotype B (n = 24). Most dogs were substage b (n = 25) with higher prevalence of haematochezia (n = 20). One dog had surgery only. Thirty dogs received chemotherapy; amongst them 13 had surgery or radiotherapy. Progression free survival (PFS) was 1318 days and disease-related median survival time (MST) was 1845 days. Fourteen dogs were alive at the end of the study with a median follow-up time of 684 days (3-4678 days). Younger dogs had longer PFS (P = 0.031) and disease-related MST (P = 0.01). Presence of haematochezia corresponded with longer PFS (P = 0.02). Addition of local treatment to chemotherapy did not significantly improve the outcome (P = 0.584). Canine PCRL has considerably longer PFS and MST than other forms of non-Hodgkin's lymphoma.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.217
GPT teacher head0.468
Teacher spread0.251 · 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

Citations21
Published2016
Admission routes1
Has abstractyes

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