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Record W4283757446 · doi:10.1177/1098612x221106353

2022 ISFM Consensus Guidelines on Management of the Inappetent Hospitalised Cat

2022· article· en· W4283757446 on OpenAlexaff
Samantha Taylor, Daniel L. Chan, Cecilia Villaverde, Linda Ryan, Franck Péron, Jessica M. Quimby, Carolyn O’Brien, Serge Chalhoub

Bibliographic record

VenueJournal of Feline Medicine and Surgery · 2022
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineMalnutritionIntensive care medicinePoor AppetiteFeeding tubeIntervention (counseling)AnorexiaPsychological interventionAppetiteNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PRACTICAL RELEVANCE: Inappetence may have many origins and, as a presenting sign or observation in the hospitalised patient, is common in feline practice. Nutritional assessment of every patient is encouraged, to identify the need for, and appropriate type of, intervention indicated. The impact of malnutrition may be significant on the feline patient, perpetuating illness, delaying recovery, slowing wound healing and negatively impacting gut health and immunity. Delayed intervention may result in the cat's deterioration; hence prompt control of contributing factors such as the underlying illness, pain, nausea, ileus and stress is vital to optimise voluntary food intake. Management is multimodal, comprising reduction of stress, medications and assisted nutrition in the form of tube feeding or parenteral nutrition. Use of antiemetic, analgesic, prokinetic and appetite stimulant medications may restore appetite, but placement of feeding tubes should not be delayed. Feeding tubes are generally well tolerated and allow provision of food, water and medication with minimal stress, although clinicians must be aware of complications such as stoma site infections and refeeding syndrome. CLINICAL CHALLENGES: Cats are vulnerable to malnutrition owing to their unique metabolism and specific nutritional requirements. Moreover, their nature as a species means they are susceptible to stress in the hospital environment, which may result in reduced food intake; previous negative experiences may compound the problem. In particular, an inappropriate clinic environment and/or handling may cause or exacerbate inappetence in hospitalised patients, with negative impacts on recovery. Postponing interventions such as feeding tube placement to await improvement, owing to clinician or caregiver apprehension, may hinder recovery and worsen nutritional deficits. EVIDENCE BASE: The 2022 ISFM Consensus Guidelines on Management of the Inappetent Hospitalised Cat have been created by a panel of experts brought together by the International Society of Feline Medicine (ISFM). Information is based on the available literature, expert opinion and the panel members' experience.

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)0.008
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0050.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0070.003
Research integrity0.0110.005
Insufficient payload (model declined to judge)0.0440.038

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 head0.156
GPT teacher head0.359
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations35
Published2022
Admission routes1
Has abstractyes

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