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Record W4385476573

Iatrogenic adrenal atrophy following oral corticotherapy is not reliably identified ultrasonographically in cats.

2023· article· en· W4385476573 on OpenAlexafffund
Céline Giron, Bérénice Conversy, Guy Beauchamp, Cyrielle Finck

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsCegep de Saint Hyacinthe
FundersUniversité de Montréal
KeywordsMedicineCorticosteroidCATSAdrenal glandPopulationAtrophyOral administrationInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Background: In dogs, corticosteroid administration is known to decrease adrenal gland height when measured ultrasonographically. However, comparable information is lacking in cats. Objectives: i) Validate that the adrenal height of our control population without corticosteroid administration was similar to previous data, ii) determine effects of dose and duration of oral corticosteroid therapy on adrenal height, and iii) determine an adrenal size threshold to differentiate cats receiving corticosteroids or not. Animals and procedures: Adult cats (N = 308) that received abdominal ultrasonographic examination(s) were retrospectively recruited and allocated into 2 groups: those with and without oral corticosteroid use. Cats receiving corticosteroids were subdivided into 6 subgroups by dose (supraphysiologic, anti-inflammatory, or immunosuppressive) and duration of therapy (≤ 1 mo or > 1 mo). Results: > 0.21), and no useful adrenal height threshold was established. Conclusion and clinical relevance: Feline iatrogenic adrenal atrophy may be difficult to establish with ultrasonography, as only cats receiving anti-inflammatory corticosteroid doses for > 1 mo had a modest (< 1 mm) decrease in adrenal height.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0010.000

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 head0.100
GPT teacher head0.314
Teacher spread0.214 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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