380 Different protocols and technologies employed to help pets lose weight.
Bibliographic record
Abstract
Obesity implements a major health risk for the canine and feline population, especially as obese dogs and cats are predisposed to devastating health complications that have a large negative impact on the quality of life and reduce life span. Moreover, the prevalence of companion animal obesity in Western countries, reaching more than 50% already, continues to increase. Weight reduction is recommended in overweight dogs and cats yet current weight loss programs tend to have disappointing outcomes and are not without risks. A first challenge in veterinary practice in the management of pet obesity, is proper assessment and identification of overweight and obese animals. Many pet owners underestimate their pet’s body condition and overestimate their activity level. Veterinary teams have trouble communicating the issue of overweight and obesity with pet owners and don’t always use available tools such as body weight, body condition score, muscle condition score and morphometry to assess pets’ body condition. Moreover, these tools are not validated for growing pets. Yet, current spay/neuter practices increase the risk for fat deposition, making this population at high risk for weight gain early in live. Veterinary teams are unaware of how to deal with this in clinical practice. Implementation of a weight loss plan is also challenging for veterinary teams, many different protocols to calculate energy requirements exist and many different veterinary therapeutic weight loss diets and over-the-counter weight management diets have been marketed. Though some research has focussed on macronutrient levels in relation to weight gain and weight loss in dogs and cats, very little research investigated nutrient requirements and the risk for development of nutrient deficiencies during energy restriction. This overview will focus on weight loss programs currently employed in veterinary clinical practice and how the coexisting challenges should direct future research.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.020 |
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".