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Record W7126647266

Hypercalcemia in a 10-Year-Old Female Spayed Miniature Schnauzer

2016· other· en· W7126647266 on OpenAlexaboutno aff
Samantha Tandle

Bibliographic record

VenueeCommons (Cornell University) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCalciumContext (archaeology)ApocrineAdenocarcinomaMedical historyMammary glandLabrador Retriever
DOInot available

Abstract

fetched live from OpenAlex

The patient, a 10-year-old female spayed Miniature Schnauzer dog, was referred to the Cornell University Hospital for Animals for hypercalcemia and a four-day history of anorexia, vomiting, lethargy, weakness, and wobbly gait. On rectal exam, a firm, non-expressible, 6 cm in diameter right anal sac mass was palpated. Initial laboratory data was suggestive of renal failure. The anal gland mass was aspirated, and cytology revealed the definitive diagnosis of apocrine gland adenocarcinoma of the anal sac. Imaging revealed evidence of local and distant cancer metastasis. Palliative treatment was elected, and pamidronate, prednisone, and fluid diuresis were used to decrease the patient’s serum calcium levels. The patient remained severely azotemic, despite aggressive fluid therapy. Serum calcium levels rapidly declined, until the patient became dangerously hypercalcemic, developed clinical signs of hypocalcemia, and required calcium supplementation. Neoplasia is the number one cause of canine hypercalcemia. This case report offers a review of calcium homeostasis, paraneoplastic hypercalcemia in the context of anal sac apocrine gland adenocarcinoma, and the medical management of calcium imbalances.

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 categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0560.090

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 head0.024
GPT teacher head0.205
Teacher spread0.181 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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