Bibliographic record
Abstract
Dear Editor,I would like to thank Dr. Davidson for sharing his thoughts on ovariohysterectomies (OVH) and ovariectomies (OE) with us. Dr. Davidson is of the opinion that in the hands of an experienced surgeon, an OVH is no less invasive than an OE.I have to disagree with this comment; although the length of the abdominal incision may not differ much between the two techniques in some dogs, with an OVH, the broad ligaments are torn over their full length, and the uterine body is ligated and transected. These structures are left intact with an OE. The result is less surgical trauma and less potential for complications. Recent research has shown that there is no sound medical reason to amputate a healthy organ (the uterus) when a female dog is sterilized.I am not trying “to seek a solution to a problem that does not exist,” as Dr. Davidson states. (See article entitled Laparoscopic Ovariohysterectomy in Nine Dogs.J Am Anim Hosp Assoc2003;39:391–396.) Although OVHs are considered routine surgeries, they are not without risk or discomfort for the patient. Trying to minimize this risk and discomfort by developing less-invasive techniques and improving existing surgical procedures is our duty as a profession. Veterinary medicine is a dynamic science, and, as professionals, we need to be ready to change our methods as new information becomes available; be it vaccination protocols or surgical techniques for sterilizing female dogs.Sincerely,
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".