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Record W4389151218 · doi:10.3389/fvets.2023.1285498

Case report: Severe asymptomatic hypertriglyceridemia associated with long-term low-dose rapamycin administration in a healthy middle-aged Labrador retriever

2023· article· en· W4389151218 on OpenAlexaboutno aff
Jeremy B. Evans, Lucy Chou, Matt Kaeberlein, Daniel Promislow, Kate E. Creevy

Bibliographic record

VenueFrontiers in Veterinary Science · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on AgingWilliam H. Donner Foundation
KeywordsHypertriglyceridemiaMedicineAsymptomaticInternal medicineAdverse effectSirolimusTriglycerideEndocrinologyPhysiologyCholesterol

Abstract

fetched live from OpenAlex

Rapamycin is an mTOR inhibitor that has been shown to extend the lifespan of laboratory model organisms. In humans, rapamycin is used at higher doses as an immunosuppressive medication to prevent organ rejection. Numerous adverse effects are seen with rapamycin treatment in humans, with one of the most common being dysregulation of lipid metabolism. In humans, this often manifests as mild to moderate serum lipid elevations, with a small subset developing extreme triglyceride elevations. This case report describes an eight-year-old, castrated male, clinically healthy Labrador retriever who developed severe hypertriglyceridemia associated with low-dose rapamycin administration over a six-month period. During this time, the dog was asymptomatic and displayed no other clinical abnormalities, aside from a progressive lipemia. Within 15 days of discontinuing rapamycin treatment, and with no targeted lipemic intervention, the dog's lipemia and hypertriglyceridemia completely resolved.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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 designCase report
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
Published2023
Admission routes1
Has abstractyes

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