Online Tools for Teaching Evidence-Based Veterinary Medicine
Bibliographic record
Abstract
Evidence-based veterinary medicine (EBVM) is of interest and relevance to veterinary practitioners. Consequently, veterinary schools take responsibility for teaching students how to appraise scientific articles and for equipping them with the skills needed to obtain and evaluate the best evidence and to apply this approach to their own cases. As part of our farm animal clinical rotation, we train students in qualitative and quantitative EBVM methods using an e-learning environment, online teaching materials, a wiki (a Web site that allows its users to edit its content via a Web browser), and face-to-face tutorials that support learning. Students working in small groups use a wiki to record details of the history, clinical presentation, diagnostic tests, herd data, and management plans for their chosen farm animal clinical cases. Using a standardized patient, intervention, comparison, and outcome (PICO) format, each group formulates a patient question based on either a proposed intervention or diagnostic procedure for the case and conducts an online scientific literature database search. The students appraise the articles retrieved using EBVM approaches and record the information in the wiki. The summation of this body of work, the group's critically appraised topic (CAT), includes the original PICO, a standardized table of the scientific evidence for the effectiveness of the intervention or diagnostic procedure, a summary statement in the form of a clinical bottom line, and their reflections upon the CAT. At the end of the rotation, students take part in a structured "CAT Club" where they present and discuss their findings with fellow students and clinicians.
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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.157 | 0.056 |
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".