MétaCan
Menu
Back to cohort
Record W3203432522 · doi:10.1638/2020-0218

USE OF SYMMETRIC DIMETHYLARGININE TO DETECT RENAL LESIONS IN FISH: A PRELIMINARY STUDY IN BROOK TROUT (SALVELINUS FONTINALIS)

2021· article· en· W3203432522 on OpenAlexaff
Juliette Raulic, Benjamin Lamglait, Claire Vergneau‐Grosset

Bibliographic record

VenueJournal of Zoo and Wildlife Medicine · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsCegep de Saint Hyacinthe
Fundersnot available
KeywordsSalvelinusFontinalisTroutBiologyRenal functionConfidence intervalPathologyInternal medicineFish <Actinopterygii>AnatomyEndocrinologyZoologyUrologyMedicineFishery

Abstract

fetched live from OpenAlex

Symmetric dimethylarginine (SDMA) is an early marker for renal lesions in mammals. The objectives of this study were 1) to establish a reference interval of SDMA in healthy brook trout, Salvelinus fontinalis (Mitchill, 1814), and 2) to assess its sensibility as a marker of renal pathology. Plasma SDMA was quantified by liquid chromatography–mass spectrometry in 25 adult brook trout, including 20 fish displaying no renal histologic lesions, and five fish displaying chronic nonactive microscopic granulomas. The fish size (P = 0.30) and weight (P = 0.12) were not statistically different among groups, nor were SDMA values (P = 0.22). However, brook trout without microscopic renal lesions tended to have lower SDMA values (no lesions: mean = 24.9 µg/dL; lesions: mean = 31.4 µg/dL). The reference interval (90% confidence interval [90% CI]) for SDMA concentration in brook trout ranged between 10.0 µg/dL (90% CI: 5.4–14.7) and 39.8 µg/dL (90% CI: 34.8–43.9). These values were higher than those previously reported in other vertebrate species. Further research is needed to evaluate the use of SDMA as a marker of renal function in fishes.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.028
GPT teacher head0.271
Teacher spread0.243 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Zoo and Wildlife MedicineSame topicFish Ecology and Management StudiesFrench-language works237,207