A Meta-Analysis of Studies of Treatments for Feline Urine Spraying
Bibliographic record
Abstract
Feline urine spraying inside the home is a common problem behaviour that owners seek advice for from veterinarians. Individual trials relating to a variety of interventions produce variable results, and to date, no consensus on the value of different treatments has emerged. This study therefore aimed to meta-analyse, current data from appropriate published clinical trials that evaluate treatments for feline urine spraying.Inclusion and exclusion criteria for study selection were predefined and methodological quality was assessed by two independent reviewers. Ten studies in nine publications that either evaluated pharmacotherapy or pheromonatherapy (the use of a synthetic analogue of the F3 facial fraction in the cat) were suitable for analysis. There was a significant (P<0.001) association between the use of any intervention and the number of cats that ceased or reduced urine spraying by at least 90%. Analysis by intervention type indicated that fluoxetine, clomipramine and pheromonatherapy may each assist in managing urine spraying beyond a placebo based intervention.This is the first time meta-analytical techniques have been used and reported to evaluate the efficacy of interventions used in veterinary behavioural medicine, and it has established confidence in the value of both conventional treatments (pharmacotherapy) and a more recently developed treatment modality (pheromonatherapy) as an adjunct to the management of this problem. It is suggested that future research into treatment efficacy for this problem uses the benchmark standard of randomised, controlled trials lasting for at least 8 weeks, with the outcome criteria of cessation of feline urine spraying or reduction by at least 90%.
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 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 | 0.034 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.081 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".