Declawing in Cat is associated with neuroplastic sensitization and long-term painful afflictions
Bibliographic record
Abstract
Declawing of pet cats is widely believed to cause chronic pain and negatively impact animal welfare, leading to bans in many jurisdictions. However, little is known on how post-declaw pain develops and affects feline well-being. Existing data often fail to account for other sources of chronic pain, such as osteoarthritis (OA), which affects most aging cats. Here, the aim was to distinguish chronic post-declaw pain from OA-related pain. A secondary analysis of eight studies on feline OA was conducted, comparing somatosensory, biomechanical and functional assessments between healthy control cats, declawed OA (DOA) cats, and non-declawed OA (NDOA) cats. DOA cats exhibited significant somatosensory alterations (hyperalgesia and allodynia) and greater biomechanical (worse in heavier cats) and functional impairments, compared to NDOA cats. The alterations were not dependent on the number of declawed paws (two forelimbs vs. four paws). This alarming phenotype was associated with objective nervous conduction abnormalities indicative of worsened axonopathy in DOA cats. Our findings highlight the impact of chronic post-declaw pain and support the need to develop therapeutic strategies to alleviate chronic pain in DOA cats and highlights the pertinence of establishing a global ban of this elective procedure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".