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Record W3037604587 · doi:10.1177/1098612x20932267

Factors affecting survival to discharge in 53 cats diagnosed with uroabdomen: a single-centre retrospective analysis

2020· article· en· W3037604587 on OpenAlexaff
Samuel J Hornsey, Zoë Halfacree, Elvin Kulendra, Sarah Parker, Nicola Kulendra

Bibliographic record

VenueJournal of Feline Medicine and Surgery · 2020
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineCATSCreatininePresentation (obstetrics)Retrospective cohort studyEtiologyUrineClinical significanceReferralUrinary systemSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to assess outcomes in cats diagnosed with uroabdomen at a single referral centre. METHODS: Fifty-three cats diagnosed with uroabdomen at a veterinary teaching hospital were identified between June 2003 and September 2016. Data collected included signalment, presenting signs, aetiology, location of rupture, presence of concurrent injury, outcome of urine culture, presence of uroliths and packed cell volume (PCV)/creatinine/potassium levels at presentation. Cats managed medically and surgically were included, and the use of urinary catheters, cystotomy tubes and abdominal drains were recorded. It was determined if patients survived to discharge or if they were euthanased or died. RESULTS: = 0.03) were shown to be significantly correlated with survival to discharge. Sex, age, location of rupture, presence of uroliths, outcome of urine culture, presence of concurrent injury, potassium at presentation and PCV at presentation were not associated with survival to discharge. There was no difference in survival between cats that were medically or surgically managed. CONCLUSIONS AND RELEVANCE: Cats that develop uroabdomen have a good chance of survival. Electrolyte and biochemistry values should be assessed at the time of presentation, in addition to the presence of concurrent injury.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.014
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.147
GPT teacher head0.327
Teacher spread0.180 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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