Feline friendly POCUS: how to implement it into your daily practice
Bibliographic record
Abstract
PRACTICAL RELEVANCE: Cats are great pretenders; they often hide illness until they are critical. This makes patients of this species challenging to assess and manage in the emergency setting where quick and stress-free diagnosis and treatment are necessary. Veterinary point-of-care ultrasound (POCUS) is a rapid, evidence-based, non-invasive, repeatable, cage-side ultrasonographic examination designed to answer clinically driven questions without compromising feline wellbeing. Integrating feline friendly POCUS as an extension of the physical examination to streamline diagnostic and therapeutic interventions, thereby limiting stress and improving overall patient care, is advocated by the authors of this article. EQUIPMENT: Given the multitude of ultrasound machines and probes available that are portable, meaning they can be moved around the clinic and used patient-side, it should be possible for most practitioners to integrate POCUS into daily practice. The authors' preferred equipment for feline POCUS is a microconvex probe and a portable machine with a fixed pre-set. This set-up allows the clinician to complete all POCUS (abdominal, lung and pleural space, and heart) without needing to move the patient, change probes or restrain the patient in a particular position, ultimately saving time, personnel and cost while maintaining patient comfort and safety. AIM: This review aims to serve as a valuable resource for veterinarians seeking to improve their feline patient care through the judicious utilisation of POCUS. In this article, the complex challenges posed by cats are addressed, and the different POCUS techniques, applications and clinical recommendations are discussed. EVIDENCE BASE: This review draws on the published literature, as well as the authors' own collective experience when providing recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".