Bibliographic record
Abstract
Topical glucocorticoids (TGC) are among the oldest and most efficient treatments to control inflammatory and immune-mediated skin disorders (1). In veterinary medicine, TGC are less attractive than in human medicine due to the density of hair and pet behavior (pets often lick the area where the topicals are applied). Moreover, the extent of body regions to be covered and the cost of topicals might, in some cases, be considered by veterinarians as limitations for the use of TGC (2). Ear disease is probably the most common reason to prescribe TGC in veterinary medicine. The greatest advantage of using TGC is the few adverse effects associated with this route of administration (2–5). Selecting the proper TGC is not easy. Indeed, whether the goal is to treat otitis externa or to address inflamed skin, the TGC chosen will affect the outcome since they vary in potency, formulation, and side effects. Properties of topical corticosteroids Anti-inflammatory properties vary according to the corticosteroid used. Although several mechanisms of action have been proposed, the exact mechanism of action of TGC is unclear. Some studies have shown that TGC impact proinflammatory molecules and mediators of pruritus, inflammation-associated nerve hypersensitivity, as well as recruitment of inflammatory cells. Topical glucocorticoids interfere with the inflammatory process and pruritogenic pathway by inhibiting the arachidonic acid pathway, some inflammatory cytokines and growth factors, and decreasing the expression of some adhesion molecules (3,6). The anti-inflammatory potency is often proportional to the ability of the corticosteroid to cause suppression of the hypothalamic-pituitary-adrenal axis and the severity of its side effects (3). Interestingly, the liver must activate the cortisone and prednisone in cortisol and prednisolone, respectively, to be metabolically active. Their topical use is therefore inappropriate (3). In North America, TGC are divided into 7 classes based on their ability to cause vasoconstriction (skin blanching in healthy people) (1,7). Class 1 contains the most potent molecules, whereas class 7 groups the weakest molecules (8). The beneficial effects obtained, as well as the side effects, also vary according to the vehicle of the product, the site of application, the disease being addressed, and individual response (1).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".