Abstracts of the scientific communications presented at the 34th European Veterinary Dermatology Congress Organized by ESVD‐ECVD, Gothenburg, Sweden, 31 August–2 September 2023
Bibliographic record
Abstract
Intralymphatic immunotherapy (ILIT) has been successfully used in both human and veterinary medicine for years and has emerged as a symptom-reducing and safe immune treatment for allergic diseases.Initially, ILIT was mostly administered by ultrasound guidance in the lymph nodes as described in human studies, and palpation-based injections became more popular among veterinary surgeons.However, human data show that precise injection into the lymph node is mandatory and injection quality clearly correlates with clinical response.The aim of this study was to assess whether the injection method (by ultrasound guidance versus palpation-based) correlated with clinical response in canine atopic dermatitis (cAD) patients.A total of 129 CAD cases treated with ILIT between 2014 and 2022 were retrieved from the hospital clinical database.Included dogs had to have received at least three intralymphatic injections administered either by palpation (PB-ILIT) or ultrasound guidance (U-ILIT).Those cases were retrospectively assessed and compared regarding clinical response to ILIT.Of 109 dogs, 84 received U-ILIT and 25 received PB-ILIT.Significantly more dogs responded to U-ILIT (60.7%) than PB-ILIT (28%).As described in human medicine, this study confirms that positive clinical response seems to depend on successful ILIT injections and low-quality injections must be acknowledged as a possible reason for ILIT failure in dogs.Further prospective and controlled studies are necessary to confirm these results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.215 | 0.108 |
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".