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Record W1996038728 · doi:10.1136/inp.d5746

Mammary mass in an overweight dog

2011· article· en· W1996038728 on OpenAlexaboutno aff
Rachel A. Casey

Bibliographic record

VenueIn Practice · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWeight lossOverweightAnimal welfareMedicineWelfareFamily medicineObesityInternal medicinePolitical scienceLawBiology

Abstract

fetched live from OpenAlex

A client brings in a nine‐year‐old female neutered labrador to ask your advice about a mammary mass. The mass is relatively small and well defined, and you consider it of value to biopsy and/or remove it. However, the dog is 33 kg (ideal weight 15 to 16 kg), struggles to walk and pants after coming from the car park to the waiting room. Looking at previous records, you see that the owner has not visited the practice for several months. At the last visit, they had been to two weight loss clinics and the dog's weight was 27 kg. A note on the record suggests that the owner was reluctant to follow the advice given or to change the dog's food from the working dog diet it was on, and could not afford a prescription diet. When you mention the weight problem and the need for the dog to lose weight before surgery is considered, the client becomes aggressive, tells you ‘the dog is on a diet and the weight problem is under control’ and refuses to discuss attending weight clinics again or changing food. You feel that the welfare of the dog is compromised, and mention this to the client, who becomes angrier and storms out of the practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.035
GPT teacher head0.370
Teacher spread0.334 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2011
Admission routes1
Has abstractyes

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