MétaCan
Menu
Back to cohort
Record W4409119890

Nutritional secondary hyperparathyroidism and subsequent fibrous osteodystrophy in a 3-year-old dromedary camel.

2025· article· en· W4409119890 on OpenAlexaff
Amanda Jaimie Butler, Briar Spinney, Laura R. Perry, Andrea Bourque, William B. Stoughton

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsOsteodystrophySecondary hyperparathyroidismRenal osteodystrophyHyperparathyroidismMedicineInternal medicineParathyroid hormoneCalcium
DOInot available

Abstract

fetched live from OpenAlex

A 3-year-old female dromedary camel was referred as an urgent case for evaluation of chronic weight loss, facial deformity, and hind-limb lameness. On initial examination, the camel was emaciated, with bilateral masses protruding from the maxillary and mandibular bones and extending into the oral cavity; the lameness could not be assessed due to recumbency. Clinical pathology and fecal flotation findings were consistent with secondary nutritional hyperparathyroidism, hypovitaminosis D, marked anemia, hypoproteinemia, and parasitism. The camel was euthanized based on the presumptive diagnosis of fibrous osteodystrophy, which was confirmed on postmortem examination. Preventative strategies were recommended for future care of dromedaries and included camel husbandry with adequate ultraviolet light exposure, adequate nutrition, appropriate anthelmintic control programs, and vitamin D supplementation. Key clinical message: The cause of fibrous osteodystrophy in camels can be multifactorial and include secondary nutritional hyperparathyroidism, hypovitaminosis D due to inadequate exposure to ultraviolet light or intake, and parasitism. Specific nutrient requirements, sun exposure, and anthelmintic protocols are essential for camels living in North America.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.485

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 head0.007
GPT teacher head0.215
Teacher spread0.209 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicDigestive system and related healthFrench-language works237,207