Bibliographic record
Abstract
Dear Editor, Dr. Rollin’s comments on the practice of “defensive medicine” (Can Vet J 2011;52:1048–1050) are absolutely spot on: veterinarians appear to be becoming more concerned with keeping their legal advisor happy than actually curing animals. The lecture by Dr. Forsgren that Dr. Rollin alludes to should be required curriculum for all veterinary professionals everywhere. I would absolutely love to hear that lecture because it speaks to my own deeply held fears that “defensive medicine” is becoming a cancer, eating at the heart of our ability to provide timely, practical, and affordable veterinary service. Dr. Forsgren’s comments about veterinary medicine falling into the trap of ordering a plethora of expensive tests to cover ourselves against liability reminds me of something my father told me 25 years ago. My father was an ophthalmic surgeon with a commission in the Royal Canadian Air Force for much of his career. His comment on extensive medical testing was, “You can do all the reconnaissance you wish, Boy, but in the end, to take a beachhead, you have to land soldiers.” At the time, his comment was a mystery, but after 25 years of practice, I now completely understand: you can do all the testing you want, but in the end, you have to take timely, appropriate action to cure the disease. Sometimes, this means making “best guess” decisions on incomplete diagnostics, but in the end, that is our job: to make decisions and, yet, to live with the consequences. In life, everything has consequences, even doing nothing.
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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.048 | 0.060 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".