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Record W2018253390 · doi:10.5326/jaaha-ms-5603

Concurrent Splenic and Right Atrial Mass at Presentation in Dogs with HSA: A Retrospective Study

2011· article· en· W2018253390 on OpenAlexaff
Sarah E. Boston, Geraldine Higginson, Gabrielle Monteith

Bibliographic record

VenueJournal of the American Animal Hospital Association · 2011
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMedicinePericardial effusionMetastasisRetrospective cohort studyOdds ratioHemangiosarcomaAbdominal ultrasoundInternal medicineGastroenterologySurgeryCancer

Abstract

fetched live from OpenAlex

The objective of this retrospective study was to evaluate the presence of concurrent splenic and cardiac hemangiosarcoma (HSA). Dogs were divided into two groups: group 1 included 23 dogs with splenic HSA, and group 2 included 31 dogs with a cardiac HSA. All dogs were fully assessed for metastasis with thoracic radiography, abdominal and/or cardiac ultrasound, and/or postmortem examination. Two dogs (8.7%) in group 1 had a concurrent cardiac mass. Neither of these dogs had pericardial effusion, and both were golden retrievers. Thirteen of the dogs in group 1 presented with a hemoabdomen. Concurrent intra-abdominal metastasis was noted in seven dogs. In group 2, 9/31 (29%) of the dogs had a concurrent splenic HSA, and 13/31 (42%) of the dogs had evidence of metastasis to another site. There was a significant association between age and the presence of nonsplenic metastasis (odds ratio, 0.457). The rate of concurrent right atrial mass detected by cardiac ultrasound in dogs with splenic HSA was 8.7%, which is less than previously reported. For dogs with right atrial HSA, the risk of metastasis to nonsplenic sites decreases with age.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.319
Teacher spread0.299 · 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

Citations52
Published2011
Admission routes1
Has abstractyes

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