WSAVA Therapeutic Guidelines
Bibliographic record
Abstract
This list of essential medicines is presented by members of the WSAVA Therapeutic Guidelines Group (TGG) following extensive internal and external peer‐review. Internal peer‐review was provided by TGG members and its subcommittees, whereas external peer‐review was performed by board‐certified individuals and other WSAVA working/guideline groups. The first draft of this document was presented at the WSAVA annual meeting in Toronto (2019) followed by a three‐month audit during which WSAVA member affiliates were asked to provide comments, suggestions and overall feedback. These were then carefully considered by the TGG. The final list is a product of several rounds of revision and based on expert consensus. \n \nThis list of essential medicines should allow veterinarians to provide proper preventive care and treatment of the most frequent and important diseases in dogs and cats while maintaining appropriate animal welfare standards. The purpose of the list is to improve and facilitate regulatory oversight for ensuring appropriate medicines availability, drug quality, use and pharmacovigilance, while mitigating the growing black/counterfeit market of pharmaceutical products. The list of essential medicines is not intended to define what medicines should be always available within the clinic/hospital; rather that veterinarians should have ready access to these (medicines) if required for the prevention and treatment of specific diseases and conditions. Additionally, the committee understands that there is no “one‐size fits all” and that there may be specific medicines used for endemic/epidemic diseases in some countries that the list does not cover. For example, the essential antimicrobials were defined as those medicines that are recommended as first line agents for treatment of at least one common disease condition.
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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.117 | 0.077 |
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".